MICP treated sand: Insights into the impact of particle size on mechanical parameters and pore network after biocementation

MICP treated sand: Insights into the impact of particle size on mechanical parameters and pore network after biocementation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MICP treated sand: Insights into the impact of particle size on mechanical parameters and pore network after biocementation Niklas Erdmann, Susanne Schaefer, Torben Simon, Andreas Becker, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4489051/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Sep, 2024 Read the published version in Discover Materials → Version 1 posted 10 You are reading this latest preprint version Abstract Microbiologically Induced Calcium Carbonate Precipitation (MICP) is a technology for improving soil characteristics, especially strength, that has been gaining increasing interest in literature during the last few years. Although a lot of influencing factors on the result of MICP are known, particle size and shape of the particles remain poorly understood. While destructive measuring of compressive strength or calcium carbonate content are important for the characterization of samples these methods give no insight into the internal structures and pore networks of the samples. X-ray microcomputed tomography (micro-CT) is a technique that is used to characterize the internals of rocks and to a certain degree MICP-treated soils. However, the impact of filtering and image processing of micro-CT Data depending on the type of MICP sample is poorly described in the literature. In this study, single fractions of local quarry were treated with MICP through the ureolytic microorganism Sporosarcina pasteurii to investigate the influence of particle size distribution on calcium carbonate content, unconfined compressive strength and the reduction of water permeability. Additionally, micro-CT was conducted to obtain insights into the resulting pore system. The impact of the Gauss filter und Non-local means filter on the resulting images and data on the pore network are discussed. The results show that particle size has a significant impact on the result of all tested parameters of biosandstone with lower particle size leading to higher strength and generally higher calcium carbonate content. Micro-CT data showed that the technology is feasible to gain valuable insights into the internal structures of biosandstone but the resolution and signal-to-noise ratio remain challenging, especially for samples with particle sizes smaller than 125 µm.. Microbiologically Induced Calcium Carbonate Precipitation (MICP) Sporosarcina pasteurii X-ray microcomputed tomography (micro-CT) image analysis Gauss filter Non-local Means filter pore network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Article highlights Microbiologically Induced Calcium Carbonate Precipitation is more effective for smaller sand particle sizes Smaller particle size of sand lead to higher permeability reduction, higher compressive strength and higher calcium carbonate contents in biosandstone X-ray microcomputed tomography and image analysis is sufficient to characterize the pore system of biosandstone and detect uneven biocemented areas Usage of different digital filters influence resulting images and data from X-ray microcomputed tomography 1 Introduction Microbiologically Induced Calcium Carbonate Precipitation (MICP) is a technology that got more and more attention in research during the last years [ 13 ]. Through different microbiological pathways calcium carbonate is precipitated. The most commonly used pathway is still the ureolytic MICP. Ureolytic microorganisms hydrolyse urea to ammonium-ions and carbonate-ions (Eq. 1) while the latter will be precipitated in the presence of calcium-ions as calcium carbonate (CaCO 3 ) (Eq. 2). \(CO{\left(N{H}_{2}\right)}_{2}+ {2H}_{2}O\to C{O}_{3}^{2-}+2 N{H}_{4}^{+}\) Eq. 1 \(C{O}_{3}^{2-}+C{a}^{2+}\to CaC{{O}_{3}}_{\left(s\right)}\) Eq. 2 This CaCO 3 can form bridges between particles and act as a binder and can therefore improve mechanical parameters of different soil types. Researchers have shown the potential of MICP to improve compressive strength [ 21 , 39 ], liquefaction resistance [ 28 , 29 ] and resilience against wind erosion [ 10 ] of soils. Furthermore, it can be used to improve compressive strength and reduce water permeability [ 6 , 23 ] of existing construction materials. Through this process it is also possible to produce novel, artificial sandstone like, materials. Although MICP based materials are not yet ready to be used for construction, they can be used for research purposes [ 20 ] opens up a now field of study. Regardless of the intended use case of biocemented soils, the homogeneity of calcium carbonate distribution in the sample is important to evaluate the quality of the cementation. Most studies use cyclic application of a calcination solution (Urea/Calcium salts) and Bacteria suspension (ureolytic microorganisms) to achieve biocementation. This often leads to clogging and inhomogeneous samples. It is possible to estimate sample homogeneity through measurements of calcium carbonate content or compressive strength content in different sections of the biosandstone which has been done several times in literature [ 7 , 37 , 38 ]. But these results are limited to sections of consolidated samples and do not yield information about small local inhomogeneities or uniformity from the outer layers of the sample to the core. Since mechanical parameters of biosandstone correlate with the calcium carbonate content in the sample [ 9 , 38 , 42 ], reproduceable results for MICP will therefore largely depend on achieving a homogenous calcium carbonate distribution. Research has described several biological, chemical and physical parameters that impact the final results of MICP [ 2 , 13 ]. While a lot of these parameters like type and concentration of bacterial cells, temperature, composition of calcination solution, etc. can be controlled, the influence of the particle size distribution of sand dictates if the locally available sand source is suitable for MICP. Although research on the influence of particle size and shape of sand acknowledges the importance of these parameters on the result of MICP the understanding of these effects remains limited [ 20 ]. Measuring mechanical parameters and calcium carbonate content of biosandstone after MICP can only give limited insight in intrinsic effects that lead to these observed characteristics. It is therefore necessary to improve our understanding of the internal structures and the pore network of biocemented materials and their resulting influence of e.g. unconfined compressive strength (UCS) or water permeability By using X-ray microcomputed tomography (micro-CT) it is possible to analyze materials destruction free. Therefore, X-ray images of the samples are taken based on the attenuation of X-rays due to density differences, differences in atomic numbers or phase boundaries. After reconstruction, images with distribution of grey values can be analyzed by 2D and 3D image analysis. This makes it possible to gain detailed insights into the inner structure of these samples. Scientists have analyzed different types of natural sandstone with Xray CT [ 15 , 25 , 27 ] also MICP was characterized by CT [ 1 , 22 , 34 ] .The examinations of MICP using CT mainly comprised the sandstone structure, the pore network, the permeability [ 1 , 22 ], creation of cracks and potential of crack sealing [ 40 ], or degree of calcite precipitation [ 1 ]. Various procedures for determining the aforementioned parameters based on image analysis are described in literature. There are different digital filters used to process the CT images and get better signal to noise ratios. At first, the denoising principle based on the replacing of color pixels or grey values with the average of nearby pixels [ 5 ]. By the use of filters, the grey values are processed e.g. with a normal distribution (Gauss filter) or a median of the grey values in the image window or section (Median filter) [ 24 ]. Because pixels with same grey value have no reason to be close at all and therefore, the Non-local Means filter uses the entire image for comparison of each pixel [ 5 ]. Also weighing factors are used at this filter [ 26 ]. For image analysis of CT images of sandstone or MICP sandstone Kirkland et al. [ 19 ] and Ming et al. [ 17 ] use Gauss filter, while Zhao et al. [ 43 ] use a median filter. In contrast, Schlüter et al. [ 26 ], Schmitt et al. [ 27 ], Thomson et al. [ 35 ], Sun Bing et al. [ 33 ], Peltz et al. [ 18 ] and Su Yang et al. [ 32 ] use the non-local means filter (NLM). Gong Nie et al. [ 15 ] show that the images look very different when Gauss, NLM, anisotropic diffusion or mean filters are used. This research group found the NLM filter to be the most suitable and carried out the image analysis with it. What all these publications have in common is that the results without/with the use of the filter or the use of different filters are not shown in their effects on the image analysis results, but the filters are only considered as part of the image analysis. Filters have an influence of the grey value distribution in images by e.g. shifting the distribution and therefore have an impact of what remains for the subsequent image analysis. This paper gives both, an overview about the visual effects of the applied filters but also the differences in resulting pore network of the MICP sandstone. In this work, we present the influence of different grain sizes of sand on calcium carbonate content, water permeability and unconfined compressive strength (UCS). Simultaneously utilizing X-ray microcomputed tomography (micro-CT) to gain insight into the biosandstone and its internal pore network. Additionally, a comparison of different digital filters was performed to evaluate their influence on the quality of the image processing of CT images. 2 Materials and Methods 2.1 Cultivation of Sporosarcina pasteurii During this study, Sporosarcina pasteurii (ATCC 11859) was used as an ureolytic microorganism for MICP. For cultivation of S. pasteurii the culture medium for according to ATCC was used. The medium contained 20 g L − 1 yeast extract (Carl Roth, Germany), 15.75 g L − 1 TRIS-buffer (Carl Roth, Germany) and 10 g L − 1 ammonium sulfate (VWR International, USA). For media preparation TRIS-Buffer was adjusted to pH 9.2 and then split into two parts: ammonium sulfate was added to one part and yeast extract to the other. The two parts were then separately autoclaved and after cooling combined under sterile conditions. S. pasteurii was incubated in 500 mL erlenmeyer flasks without baffles with a filling volume of 200 mL at 30°C and at 250 rpm (Multitron S-000115689, Infors HT, Switzerland) until the cells reached late exponential phase. The cells were then harvested and washed three times with 0.9% NaCl. Centrifugation after each washing step was performed at 10°C for ten minutes at 4000 rpm. Cells were then resuspended in 0.9% NaCl (Carl Roth, Germany), to an OD 600 of 3.5 and stored at 4°C until cementation experiments. Additional to OD 600 the ureolytic activity of S. pasteurii was determined by conductivity method as described by Whiffin [ 36 ]. 2.2 Sand types for cementation experiments For cementation experiments two sands were investigated. SH (Sand Haltern) is a silica sand from Haltern (Quarzwerke Haltern GmbH, Haltern, Germany) that was used in previous studies for MICP [ 14 ]. SP (Sand Picard) is waste sand from a Sandstone Quarry (Carl Picard Natursteinwerk GmbH, Krickenbach, Germany). The particle size distribution (PSD) of 500 g of the sands was determined after wet sieving and drying for 24 h at 105°C with sieves (Diameter 200 mm, RETSCH GmbH, Germany) with mesh size between 0.063 mm and 16.0 mm (Table 1 ). Each sample is named by the name of the sand type (SH, SP) and the particle size (0063, 0125, 0250, 0500, Mix) for example SP0250 is the fraction with d max =0.250 mm from the sand from the Sandstone quarry Picard (SP). Additionally, the particle size distribution (PSD) of SH was replicated from fractions of the Sands SP to compare different sand types with nearly identical PSD. This recreation is called “SPMix”. Table 1 Overview of PSD from the Sand SH, SP, and SP after mixing to achieve the same PSD as SH. d min and d max give the lowest and highest range of particle size that can pass the sieves. Particle size in mm Mass percentage of the particle fraction in % d min d max SH SP SP Mix 0.0 0.063 4.61 15.28 4.61 0.063 0.125 1.87 6.40 1.87 0.125 0.25 75.81 30.41 75.81 0.25 0.5 17.7 39.31 17.7 0.5 1.0 0.01 7.56 0.01 1.0 2.0 0.00 1.03 0.00 2.0 4.0 0.00 1.18 0.00 4.0 8.0 0.00 2.25 0.00 8.0 16.0 0.00 2.33 0.00 2.3 Preparation of sand columns Sand columns were prepared in two sizes to meet the different size requirements of the analytical methods. For unconfined compressive strength and water permeability tests sand columns were prepared from 50 mL reaction tubes, diameter 27 mm (Greiner centrifuge tubes, Sigma-Aldrich). The bottom part of the tube was cut off and five holes (diameter 6 mm) were drilled in the cap to allow liquids to flow freely through the column. Prior to filling the columns with sand, the holes were covered with filter paper (40 µm. Qualitativ Filterpapier 417, VWR International). The columns were filled with 40 g sand. The sand was covered with filter paper as well to prevent disturbing the surface of the sand column during MICP. Afterwards, the columns were closed with a plug containing a hole for the application of bacteria suspension and calcination solution during MICP. For X-ray microtomography smaller samples were necessary. Therefore, columns were prepared from the top part of 10 mL standard pasteur pipettes not graded 300 mm (Carl Roth, Germany). They were cut at the top and the bottom part was shortened to 1 cm to allow better handling of the columns. The outlet of the pipette was sealed with cotton. The conical part of the pipette was filled with sand SH [ 2 ] as drainage. A non-woven all-purpose cloth (84% viscose, 16% polypropylene, W5, Germany) was placed on this drainage sand. Then 5 g of sand was filled in and the sand was covered with filter paper. 2.4 MICP Protocol for biocementation MICP treatment was conducted through sequential injection of resuspended cell suspension (OD 600 = 3.5) and a calcination solution containing 1492 mM urea (Fisher Scientific, USA) and 1392 mM calcium chloride dihadrate (Carl Roth, Germany). Initially, 50% pore volume of cell suspension was applied on the column with a peristaltic pump (IPC 8, ISMATEC, Switzerland) with a flow rate of 3 mL∙min -1 . After 2 hours 50% pore volume of calcination solution was applied at 3 mL∙min -1 . This treatment was followed by 5 hours of incubation at 25°C. This treatment is considered as one cycle with 10 cycles in total for each column. 10 Cycles were chosen because preliminary experiments showed that with 10 cycles we can achieve a reasonable strength of the samples while clogging of the samples started to occur from 10 cycles onwards which lead to uneven cementation and a wider variance of compressive strength (Supplement 7). After the last cycle columns were washed with 300% pore volume of distilled water to flush out residual salts and cells and dried for 72 hours at 60°C. After curing at room temperature for 14 days unconfined compressive strength and permeability tests were performed. 2.5 Determination of calcium carbonate content For calcium carbonate determination 5 g of the sample were mixed with 20 mL of 2 M hydrochloric acid. Once no production of CO 2 gas could be visually observed the reaction was given another hour to dissolve any remaining calcium carbonate. Afterward the samples were diluted in 2 mM nitric acid and the content of soluble calcium ions was determined by ion exchange chromatography (n 930 Compact IC Flex, METROHM GmbH & Co. KG). Based on the concentration of soluble calcium ions the total amount of calcium carbonate in the sample was calculated and the content in % calculated as the quotient of calcium carbonate about the initial sample weight. 2.6 Image acquisition by X-ray microcomputed tomography To get a better resolution on micro-CT. the smaller samples (diameter about 10 mm) were used as mentioned in Chap. 2.3. Depending on the sample diameter, a scan resolution of 6.79 µm/pixel was achieved. With 90 kV and 110 µA as well as a 0.25 mm aluminum filter and an exposure time of 3012 ms the samples were X-rayed at 0.3 ° angle steps. Scan duration was about three hours in a skyscan 1272 X-ray microtomograph (Bruker, Kontich, Belgium). To reduce noise, a frame averaging of 4 was applied. Thereafter the reconstruction was performed by using NRecon 2.0.0.5 (Bruker, Kontich, Belgium). Here postalignment was 1, no smoothing was applied, ring artefacts reduction was set to 50 although there were less to none visible. Min and max image conversion were set to 0 resp. 0.077945. Images were reconstructed as tiff 16 images. Table 2 Overview of micro-CT samples. Sample Label Particle Size in mm Weight in g Calculated object height in Volume of Interest in mm SP0063 > 0.063 5.01 10.054 SP0125 > 0.125 5.02 10.570 SP0250 > 0.250 5.03 10.183 SP0500 > 0.500 5.02 10.257 2.7 Determination of water permeability Water permeability was determined as the coefficient of permeability \({k}_{\text{f}}\) by Darcy´s law. The measurement was conducted at test station compliant with DIN EN ISO 17892-11 [ 12 ]. The test station includes a pressure-controlled tank made of polyvinyl chloride. The pressure in the pressure tank is set to a constant value of p = 0.5 bar by a compressor, which is regulated by a pressure gauge. The container is a tube that is screwed to a teflon top and bottom section using threaded rods. The samples are measured using modified centrifuge tubes. These are positioned in the centre of the pressure vessel. Filter disks made of sintered bronze with a layer thickness of 2 mm are placed above and below the sample or the loose bulk material. The centrifuge tube is screwed into a thread in the base section. The upper opening of the tube is closed using a silicone plug in which a silicone tube is positioned. The flow rate of water is set manually in the range 2–200 mL·min -1 . Once a stationary flow regime through the sample has been established, the amount of liquid passed per time can be measured. The measurement duration of the liquid volume was five minutes with one measurement being carried out every minute. The quantity of liquid that passes through is collected in a container and the mass was determined using a digital scale. The coefficient of permeability \({k}_{\text{f}}\) was calculated with the equations of Darcy´s law (Eq. 3–5). \({\Delta }{h}_{\text{W}}=i\cdot {H}_{\text{P}}\) Eq. 3 \(v= \dot{V}\cdot {A}^{-1}\) Eq. 4 \({k}_{\text{f}}=v\cdot {i}^{-1}\) Eq. 5 Using the measured sample height \({H}_{\text{P}}\) and a self-selected hydraulic gradient \(i\) , the hydraulic pressure difference \({\Delta }{h}_{\text{W}}\) can be calculated (Eq. 3). This is required to set the pressure applied to the pressure gauge. The flow velocity \(v\) is then calculated using the volume flow \(\dot{V}\) , which is determined by the individual measurements, and the cross-sectional area \(A\) of the biosandstone sample (see Eq. 4). Finally, the permeability coefficient \({k}_{\text{f}}\) is determined using the flow velocity \(v\) and the hydraulic gradient \(i\) (see Eq. 5). 2.8 Determination of unconfined compressive strength Unconfined compressive strength (UCS) was determined with a universal testing stage (Compression Testing Machine Type 812, FHF Strassentest, Germany). Before measurement the top of samples were sanded flat to achieve an even surface for force application during measurement. The final height of the samples \({H}_{\text{P}}\) were 40 ± 5 mm. Force was applied with a traverse speed of 2 mm·min -1 until the sample broke. From the resulting stress-strain diagrams the UCS was determined. 2.9 Image analysis First of all, the Volume of Interest (VOI) is determined. Due to the sample setup inside the pipette with the loose sand or MICP sandstone between filter paper and non-woven cloth, the amount of images in one stack varies. The separation objects filter paper or non-woven cloth can lie in an angle and therefore, the images for image analysis have to be chosen wisely. For the analysis, only images without pipette walls, filter paper or non-woven cloth are used. Despite efforts to keep deviations as small as possible, the mean of images in one stack is about 1511 ± 28 images. This results in an object height of 10.266 ± 0.19 mm for MICP sandstone characterized with micro-CT. After the VOI determination, the image analysis can be started. Therefore, either no filter, Gauss filter or Non-local means filter were applied. No filter gives the reference, while gauss filter is quite popular and part of many image analysis software. So it is a good option to compare it to other digital filter results. And non-local means filter was chosen because of the fact, it uses the entire image not just surrounding pixels for comparison of grey values and their adjustment. The image analysis was performed with CTAnalyser V1.20 (Bruker. Kontrich, Belgium). Gauss filtering is possible with CTAnalyser (CTAn), while Non-Local Means (NLM) has to be performed with Fiji, an open source image processing package basing on ImageJ2. In Fiji it is possible to use the Non-local means plug-in based on the work of Buades et al. [ 5 ] and Darbon et al. [ 11 ]. NLM was performed with Sigma = 15. Afterward, a lot of noise, especially in sand grains, is removed. But it has to be kept in mind, that filters maybe cause artefacts, which are then part of the image analysis and results. For the image analysis with CTAn two task lists are necessary. In the first task list, sandstone volume and porosity of the object are calculated. Afterwards a Region of Interest (ROI) shrink wrap is performed which finishes the first task list. Here, a new region of interest can be created from an existing ROI by adapting the shape of the sandstone object, which was calculated before [4]. The second task list is all about analysing pore space between sand grains. Of interest here are either connected pores, which form a coherent pore system, or the individual pores. By using bitwise and morphological operations it is possible to fully separate the connected from the individual pore system. For the connected pores a 3D-analysis was performed and the individual pores were analysed by individual object analysis. Both task lists can be found in supplementary material (Supplement 8–11) 3 Results and Discussion 3.1 Relationship between Calcium carbonate content, Water Permeability, and Unconfined Compressive Strength of biosandstone The treatment of each sand with MICP resulted in observable calcium carbonate depositions on the sand surface (Fig. 3 ). In the samples, effective calcium carbonate bridges as well as ineffective crystals that grew into void space could be observed. The former effect is the main goal of biocementation through MICP for the improvement of mechanical soil characteristics and the reason for the observed strength of sand after biocementation. The observed quantities of calcium carbonate range from 11.60 ± 0.25% g g -1 to 6.69 ± 0.59% g g -1 of total weight with an overall trend of decreasing calcium carbonate content with increasing particle size (Fig. 5 ). For each sample, there is a decrease in water permeability caused by MICP treatment. For the loose sand and samples treated with MICP, the water permeability is inversely proportional to the grain size (Fig. 4 ). The water permeability coefficient for the loose sand fraction SP0063 – SP0500 ranges from 6.37∙10 − 5 m∙s -1 to 4.55∙10 − 4 m∙s -1 and decreases through MICP treatment to 2.12∙10 − 6 m∙s -1 (SP0063) to 2.50∙10 − 4 (SP0500). The reduction of permeability ranges therefore from 96.7% (SP0063) to 45.0% (SP0500) and generally decreases with increasing particle size. The strongest decrease from 6.36∙10 − 5 m∙s -1 to 2.12∙10 − 6 m∙s -1 occurs for the smallest fraction SP0063. In contrast, the lowest decrease from 4.55∙10 − 4 m∙s -1 to 2.50∙10 − 4 m∙s -1 occurs for the largest fraction SP0500. Permeability coefficients of SH and SPMix lie between SP0125 and SP0250, which is because these fractions make up the largest proportion of this fill at 75.8% and 17.7%. Both the two sands SH and SPMix as well as the individual fractions of SP show a reduction in water permeability after MICP treatment of 45.0 to 96.7%. This reduction of permeability through MICP can be attributed to the blocking of flow paths through the sand. Especially large pore networks and channels through the sand get blocked by cementation and permeability is reduced with increasing degree of cementation [ 3 ]. The higher decrease for lower particle sizes is most likely attributed to this blocking effect. For smaller particles void space decreases which could lead to calcium carbonate crystals forming with a higher chance at pore throats leading to reduced permeability [ 20 ]. Furthermore, for biosandstones with smaller particle size a generally higher degree of cementation was observed (Fig. 5 ) which also increases the chance of pore blocking. UCS shows a similar trend for sand fractions. With increasing particle size the UCS of the biocemented samples decreased from 3.04 ± 0.73 MPa (SP0063) to 0.74 ± 0.15 MPa (SP0500) (Fig. 5 ). Since the void space between particles is bigger for higher particle sizes [ 20 ] the formed calcium carbonate crystals have a higher chance of growing into voids instead of forming effective bridges at contact points. SP0063 and SP0125 have nearly identical UCS with SP0125 with higher variances than samples with a bigger particle size which might be due to a higher chance of clogging in the sample which leads to a more uneven distribution of bacteria suspension and calcination solution and therefore uneven calcium carbonate distribution. This clogging effect is a common phenomenon of MICP calcinated sand although more prominent in sand with low permeability since bacteria suspension and calcination solution can distribute more freely through the sand and therefore the reaction is more evenly distributed throughout the samples [ 8 ]. This clogging effect is also likely the reason for the higher decrease in water permeability for smaller particle sizes as the precipitated calcium carbonate blocks pores and thereby flows paths through the sand. The decrease in calcium carbonate content with increasing particle size supports this. Samples with lower particle sizes withhold more calcium carbonate on throat points than samples with bigger particle sizes most likely due to flushing out calcium carbonate from big pores that could not attach to the surface of particles. This effect resulted in higher calcium carbonate content which subsequently resulted in higher UCS and greater reduction of permeability. It is notable, that by reconstructing the particle size distribution of SH with SP did not lead to similar UCS or reduction of permeability coefficient. With 3.04 ± 0.48 MPa and a reduction of permeability of 51.31% SPMix had a significantly higher UCS compared to SH (1.61 ± 0.48 MPa) while having a lower reduction in permeability coefficient (71.60%). This suggests that it is not solely the particle size of the sand that impacts the result but also the type of sand. Particle shape and size might be of similar importance. A study conducted by Song et al. investigated MICP-treated silica sand with different particle morphologies and gradings from three sand types sieved into four size fractions: (1.00–0.85 mm), (0.85–0.425 mm), (0.425–0.250 mm), (0.250–0.180 mm) [ 30 ]. They observed that UCS of treated spherical and near-spherical sands peaked at 5.21 MPa for particle sizes of 0.85 − 0.425 mm, whereas treated angular sands had increasing UCS with decreasing particle size. Similar to our findings, they observed an increase of calcium carbonate content with decreasing particle size for all tested sand types. They concluded that particle morphology and resultant bonding mechanisms exert critical controls in MICP-grouting that significantly affect the cementation structure and, as a result, the ensemble strength of the treated assemblage. Gowthaman et al. consolidated three well-graded sands Mizunami (d 50 = 1.6 mm), Mikawa (d 50 = 0.87 mm), and Toyoura (d 50 = 0.2 mm), and achieved compressive strengths of 1.82 MPa. 2.67 MPa and 3.98 MPa respectively [ 16 ]. Zhao et al. observed a notable reduction in permeability and improvement in UCS with the incorporation of different fine particle contents and pore ratios in silt [ 44 ]. Although our understanding of the mechanisms behind the impact of grain size and shape of particles on the process remains limited [ 20 ], the importance of particle size on UCS, permeability reduction, and calcium carbonate content of biocemented sand when choosing sand for MICP is clear. In summary, our research findings are in alignment with recent literature, indicating that the efficacy of MICP in improving UCS, reducing water permeability, and influencing calcium carbonate content is significantly affected by sand particle sizes. These insights not only validate our research outcomes but also highlight the necessity of precise knowledge of the soil that will be utilized for MICP. Although the macroscopic measurements of UCS, calcium carbonate content, and permeability hold valuable information on the efficiency of the MICP process it remains important to gather information on the internal structure of biocemented sand that cannot be explained by these measurements. CT image analysis is a tool that could help extend our understanding of the internals of biocemented sand. 3.2 CT results Images of biosandstone, created by X-ray microcomputed tomography and image analysis, were first of all processed with or without a digital filter. Afterwards, image analysis was performed with the two task lists (Supplement 8–11). Resulting images can be separated by their containing objects like sandstone, connected pore system or individual pores. A comparison of image components like sandstone, pore network, or edge areas shows: Whether or not a filter is used, the sandstone has an average proportion of about 63% of the image, while the pores have about 13.5–14.9% as can be seen in Fig. 6 . The rest are edge areas, which exist due to the round sample geometry. Images treated with Non-local means filter show in mean the highest proportion of sandstone and the lowest proportion of pore system. Without a filter, the largest proportion of pores and the smallest proportion of sandstone are present. Noise is probably still present here, which is calculated as pores after setting the threshold and analyzing the image. Table 3 shows the results of image analysis regarding the volumes of either connected pores or biosandstone, also in comparison with used filters. It can be seen that SP0063 has always the lowest volume of connected pores. The reason for this may be that the small sand grains are more densely packed than the sand grains in SP0500. The object volume of biosandstone ranges between 1963 mm 3 up to 2068 mm 3 . Differences in the volume of biosandstone regarding the used filters can be recognized. Table 3 Volumes of connected pores and biosandstone objects calculated by CT and image analysis Without filter Gauss filter NLM filter Sample Label Connected Pore Volume in mm 3 Sandstone object volume in mm 3 Connected Pore Volume in mm 3 Sandstone object volume in mm 3 Connected Pore Volume in mm 3 Sandstone object volume in mm 3 SP0063 428 1974 446 1963 418 1981 SP0125 466 2061 466 2068 462 2068 SP0250 468 1967 452 1990 453 1998 SP0500 469 1974 453 1999 440 2010 When analyzing the pore system, it must be noted that there are two ways to achieve the results: CTAn task list 1 calculates the porosity based on the sandstone object, while there is also the possibility to characterize the pore system directly to get more information. This is done by the second task list, as described in Chap. 2.9. The detection limit for pores due to the resolution must be taken into account. Figure 6 shows excerpts of the results of image analysis either of sandstone or pore network. The direct characterization of the pore network shows that there is a large network of connected pores. It can be seen that the individual pores are found more frequently in the edge areas towards the pipette wall at every used sand grain fraction. Whereas the connected pores are mainly at the center of the biosandstone. This is particularly interesting about a uniform flow of both, bacteria solution and calcination solution and has also an effect on solidification. Because the solutions should distribute homogeneously through the sand and not just pass through completely. To achieve this, the reaction time must be correct so that there is sufficient time for the cementing reaction. In addition, the cementation should be as uniform as possible through the sample to achieve reproducibility. The micro-CT images provide insight in the biosandstone and enable the detection of optimization potentials. The general visualization of biosandstone can significantly enhance our understanding of the cementation process. Notably, the edges of the biosandstone exhibit stronger calcination compared to the internal part of the sample (Fig. 6 ). This observation hints at a potential flushing-out the effect of residue bacteria cells and small calcium carbonate crystals with each new cycle of MICP treatment. Micro-CT measurements offer valuable insight and enable a deeper investigation into the differences between various cementation protocols. For future research, it could be interesting to investigate for example the effects of the contact flexible mold on the distribution of calcium carbonate crystals in the biosandstone [ 41 ]. During this cementation protocol, the sand is loaded with bacteria suspension and the sample is immersed in a bath of calcination solution. Micro-CT could help to detect how strongly pronounced a cementation gradient from the outer layers to the internal parts of the biosandstone is. Also, it helps to detect defects and large pore systems. These large pore systems can form channels and have a bigger impact on the permeability of sandstone than just the porosity [ 32 ]. Also, possible weak points in the sandstone in the form of large pore systems that impact the UCS of biosandstone can be detected by micro-CT. Although it was not possible for us to find a correlation between large pore systems and breaking points of Biosandstone due to the destructive nature of UCS testing, a coupled measurement of UCS and CT scans could be possible to detect initial crack forming. The location of these cracks in the biosandstone would help greatly to improve our understanding of cementation protocols. Whereas the individual pores have also an influence on the biosandstone strength, their influence depends on the pore volume and their number. If there are lots of big pores, the biosandstone stability of course is reduced. Examination of the individual pores shows differences in the number of pores depending on the used filter (see Table 4 ). Here, it can be seen that without a filter a lot of noise is recognized as a pore network, so the use of a sufficient filter is quite useful. The resolution of the images should also be taken into account, especially with small sand grain sizes. Because it is more difficult to distinguish whether there are pores or noise is changing the results. Table 4 Number of individual pores Without filter Gauss filter NLM filter Sample Label Number of individual pores Number of individual pores Number of individual pores SP0063 581 020 456 481 468 219 SP0125 398 861 277 628 305 105 SP0250 380 962 171 691 142 371 SP0500 420 214 136 188 100 691 Furthermore, Fig. 6 shows the individual pores as pore volume distributions. There it can be seen, especially for SP0063 and SP0125, that without the use of a filter, the distribution is shifted towards small pore diameters. Additionally, with consideration of the other curve profiles, which show fewer small pores, it can be estimated, that without a filter there is noise in the ROI images, especially for SP0125. It is also noticeable in Fig. 6 that with increasing sand grain size the amount of pores towards higher pore diameters increases. SP0250 and SP0500 show higher pore diameters differences about the used filter. Based on this data, no trends can be seen but to be sure, a higher sample size has to be analyzed. Also, it has to be kept in mind that pore volumina are calculated as spheres and the influence of the pore diameter is high. Therefore, small differences in the filtering process can lead to bigger pores and are shown as peaks in Fig. 6 . While the pores of biosandstone are mostly not in the shape of a sphere due to the shape of sand grains and their packing, the assumption of a spherical shape is a simplification to create a working model. The representation as pore distribution curves provides good possibilities for comparison over the entire sample height. In contrast, Minto et al. [ 22 ]chose to display the porosity per image along the z-axis to provide an insight into the pore structure in the biosandstone. In SP0063 there is, regardless of whether a filter was used in the image analysis, the highest proportion of individual pores with about 8.76% (NLM filter) up to 10% (without filter or Gauss filter) of total pore volume. Towards larger sand grains, the proportions become smaller and smaller until it is only 1.39% (NLM filter), 1.48% (Gauss filter), or 2.87% (without filter) for SP0500. If the entire pore system is considered, it can be seen that the volume of connected pores is lower after using the NLM filter than without the filter (see Table 3 ), despite otherwise identical treatment of the images. This illustrates the effect of the NLM filter very clearly, as the gray values are averaged here as described above. Some areas that would be counted as pores without the filter are probably influenced by NLM in such a way that they are included at a threshold of 50–255 and counted as sandstone. The use of filters in image analysis and characterization of pore systems has an impact on the results which is shown in Fig. 6 . The influence of the filter method varies for different analysis results like the amount of individual pores, volume of connected pores, or total porosity as well as sandstone volume. It is important to mention the filtering method in investigations and consider its effects on calculations. Filters are necessary to reduce visible noise in micro-CT images. The Gauss filter visually reduces noise by smoothing but still retains a high proportion of very fine pores and speckles. On the other hand, the NLM filter performs well, particularly for fractions with larger sand grains. However, it should be noted that the NLM filter produces a noticeable difference from the original image due to the way it changes the gray values, which affects the results when setting the threshold for image analysis. All in all, the use of a filter for our data is necessary to remove noise. The comparison of gauss filter and non-local means filter showed differences in resulting images. Thresholding of this images led to different amounts of sandstone volume or pore network volume. By using the non-local means filter, a more uniform grey value distribution within the sand grains could be achieved and therefore less speckles within image analysis. It has to be kept in mind, that the sigma parameter must be adjusted to the size of the sand grains depicted so that even the smallest grains of sand and pores are still sharply depicted and are not removed by blurring, if sigma is too large. We recommend using the NLM filter for the application on biosandstone, taking into account the problems and limitations mentioned above. 4 Conclusion In this study, different sand fractions from a local sandstone quarry were consolidated using MICP. The resulting biosandstones were analyzed for unconfined compressive strength, water permeability, and calcium carbonate content. Additionally, the samples were visualized by micro-CT, and the resulting pore system was analyzed. From the results, the following conclusions can be drawn: Sand fractions ranging from 63 µm to 500 µm as well as ungraded sands could effectively be treated with MICP which resulted in a reduction of permeability coefficient and measurable unconfined compressive strengths (UCS) of biosandstone Smaller particle size of sand was better suited for MICP than larger particle size. For smaller sand particles higher UCS, calcium carbonate content and reduction of permeability coefficient could be observed Particle size distribution is not the only parameter of sand affecting the efficiency of MICP. Particle shape likely impacts the MICP as well which became apparent by the different results of industrial sand and a reconstruction of the particle size distribution of industrial sand with local quarry sand When using locally available sand resources it is necessary to investigate particle size distribution before MICP treatment and eventually reconstruct a more suitable particle size distribution by recombining sand fractions X-Ray microcomputed tomography (micro-CT) is suitable for visualization of biosandstone and its pore space but appears to be limited for small particle sizes due to difficulties in differentiating between pore space and noise for certain resolutions The choice of filter is important to consider for processing the micro-CT data. Without filtering, noise is apparent in the particle size distribution curves and different filtering changes the results of the observed pore space and porosity Inhomogeneous areas of biosandstone can be visualized destruction free by micro-CT. This could help to characterize homogeneity of biosandstone in addition to segmented calcium carbonate measurements. In future studies, it could be interesting to investigate if these areas act as breaking points by combining compressive tests with micro-CT measurements Declarations Funding This project was financially supported by the Deutsche Forschungsgemeinschaft (DFG. German Research Foundation) – Project-ID 172116086 – SFB 926 and the “Landespotentialbereich NanoKat”. Acknowledgment We thank Martin Picard and the staff of Carl Picard Natursteinwerk GmbH for providing the sand necessary to conduct this research. We would also like to thank Prof. Dr. Christos Vrettos for the opportunity to use the equipment at the department for Soil Mechanics and Foundation Engineering and for his expert input on several occasions. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Code availability Not applicable Conflict of Interest The authors have no relevant financial or non-financial interests to disclose Author Contributions S.S. and N.E.conceived and,planned the experiments. Material preparation, data collection, and analysis were performed by S.S, N.E. and T.S.. The manuscript was written by S.S. and N.E. equally. All authors provided critical feedback and input for the manuscript. All authors read and approved the final manuscript. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. References Akimana RM, Seo Y, Li L et al. 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Supplementary Files SupplementaryData.docx Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2024 Read the published version in Discover Materials → Version 1 posted Editorial decision: Revision requested 26 Jun, 2024 Reviews received at journal 25 Jun, 2024 Reviews received at journal 23 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 04 Jun, 2024 Submission checks completed at journal 04 Jun, 2024 First submitted to journal 28 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4489051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313729245,"identity":"86b89b25-2182-4a03-846f-77759d03503f","order_by":0,"name":"Niklas Erdmann","email":"","orcid":"","institution":"University of Kaiserslautern-Landau","correspondingAuthor":false,"prefix":"","firstName":"Niklas","middleName":"","lastName":"Erdmann","suffix":""},{"id":313729246,"identity":"5ba09f7c-e673-4f4f-83a8-4a80e7a53500","order_by":1,"name":"Susanne Schaefer","email":"","orcid":"","institution":"University of Applied Sciences Trier","correspondingAuthor":false,"prefix":"","firstName":"Susanne","middleName":"","lastName":"Schaefer","suffix":""},{"id":313729247,"identity":"12e837ba-6789-4a94-8278-9213def1ccdb","order_by":2,"name":"Torben Simon","email":"","orcid":"","institution":"University of Kaiserslautern-Landau","correspondingAuthor":false,"prefix":"","firstName":"Torben","middleName":"","lastName":"Simon","suffix":""},{"id":313729248,"identity":"f963bfac-c039-4c90-9995-7add14229e6e","order_by":3,"name":"Andreas Becker","email":"","orcid":"","institution":"University of Kaiserslautern-Landau","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Becker","suffix":""},{"id":313729249,"identity":"ce32c7e2-69bd-4929-a88d-d627883eb09b","order_by":4,"name":"Ulrich Bröckel","email":"","orcid":"","institution":"University of Applied Sciences Trier","correspondingAuthor":false,"prefix":"","firstName":"Ulrich","middleName":"","lastName":"Bröckel","suffix":""},{"id":313729250,"identity":"fb21f32e-b47e-46e8-8b8c-6a4854d61d4f","order_by":5,"name":"Dorina Strieth","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDCCA2AEAowNQGQDlwByCWjhAak52JAGF8WrhQGiBQgONhwmrIXveO/DwwU1DPb20ocbH3/ccT7a4ADvs8cf2xhk+3FokTxz3ODwjGMMiT18ic0GB8/czt1wgN3c4GAbg/FMHNYY3EhjOMzDxpDAw8PYJnGw7XbuzAY2NiCDIXHDAXxa/jHYA7W0/zjYdg6hZT8+LbxtDIw9QFsYDrYdyO1ngNmC0y/HgFr6JBJ7zjA2S5xtS87tZ2ZjNzhzTsJ4Bg5b+I63MX/m+WZjz97D/vBDZZtdbht7G9uDijIb2X4c3ocCCSQ2MwMbmggRgI005aNgFIyCUTDcAQBG9l+17BPgawAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kaiserslautern-Landau","correspondingAuthor":true,"prefix":"","firstName":"Dorina","middleName":"","lastName":"Strieth","suffix":""}],"badges":[],"createdAt":"2024-05-28 07:51:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4489051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4489051/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43939-024-00108-3","type":"published","date":"2024-09-20T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58760644,"identity":"8ea64f02-fd10-4c6f-b410-c16250e62dd4","added_by":"auto","created_at":"2024-06-20 18:51:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":509197,"visible":true,"origin":"","legend":"\u003cp\u003eSchematics of biosandstone production. Microscopic images show the surface of loose sand and biosandstone after staining calcium carbonate crystals with Alizarin Red S (Carl Roth, Germany).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/9d55bda3ef64f19f2ba3842c.png"},{"id":58760636,"identity":"0401bd41-5a22-4b81-9d6a-81107996aec5","added_by":"auto","created_at":"2024-06-20 18:51:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":569888,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of image analysis procedure (): The analysis is divided into two task lists where the first task list is used to analyze the sandstone object, and the second one focuses on the pore network consisting of a connected pore network and individual pores.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/beb63fb5c5d1c22b2d2522f0.png"},{"id":58760609,"identity":"d9d16a12-a3e3-407a-872c-f938bd05185f","added_by":"auto","created_at":"2024-06-20 18:51:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":910838,"visible":true,"origin":"","legend":"\u003cp\u003eMicroscopic images with 500x magnification (VHX-7000, Keyence, Germany) of biocemented sand. (A) SP0063; (B) SP0125; (C) SP0250; (D) SP0500; (E); SH; (F) SPMix\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/e075ab0d0e8526526f4798be.png"},{"id":58760682,"identity":"f94d03db-728a-4c39-b980-c292bd635e25","added_by":"auto","created_at":"2024-06-20 18:51:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":240581,"visible":true,"origin":"","legend":"\u003cp\u003eWater permeability coefficient of before and after MICP treatment For different sand fractions. SH = Sand Haltern, SP = Sand Picard. The sand fractions were treated with 10 cycles of \u003cem\u003eS. pasteurii\u003c/em\u003e and calcination solution according to chapter 2.4. The error represents the standard deviation of n=6 biological replicates.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/9e9296827b9d464df248c353.png"},{"id":58760607,"identity":"cb944d01-93ca-40e0-ab12-fe50806ade67","added_by":"auto","created_at":"2024-06-20 18:51:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":285302,"visible":true,"origin":"","legend":"\u003cp\u003eUCS and CaCO\u003csub\u003e3\u003c/sub\u003e content for different sands and particle fractions. For different sand fractions. SH = Sand Haltern, SP = Sand Picard. The sand fractions were treated with 10 cycles of \u003cem\u003eS.\u0026nbsp;pasteurii\u003c/em\u003e and calcination solution according to chapter 2.4. \u0026nbsp;The error bars represent the standard deviation of n=6 biological replicates.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/2e54a1a6cab3bd17023fb8e6.png"},{"id":58760681,"identity":"7b680c35-5596-4de6-a2fb-05321ab33ee8","added_by":"auto","created_at":"2024-06-20 18:51:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1027423,"visible":true,"origin":"","legend":"\u003cp\u003eResults of X-ray microcomputed tomographic characterization and image analysis: Comparison of filter effects and the resulting sandstone object and pore network, consisting of connected pores and individual pores. These individual pores are also shown as density distributions of pore volume with wf = without filter, g = gauss filter, nlm = non-local means filter. Also, the effect of different sand grain sizes on the difficulty of image analysis can be seen in the binary images.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/0ccc4377d95f1baa49658f5b.png"},{"id":65103985,"identity":"da52470b-5d25-4efb-8291-21d6f3ac84f9","added_by":"auto","created_at":"2024-09-23 16:10:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5030226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/edfd0d8e-8d5d-4d80-b47e-d1440e03cb5d.pdf"},{"id":58760647,"identity":"4634b461-2f13-4414-a190-40e11f8cfcd4","added_by":"auto","created_at":"2024-06-20 18:51:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":211828,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-4489051/v1/540a0d4e1f4857250d95b808.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"MICP treated sand: Insights into the impact of particle size on mechanical parameters and pore network after biocementation","fulltext":[{"header":"Article highlights","content":"\u003cul\u003e\n \u003cli\u003eMicrobiologically Induced Calcium Carbonate Precipitation\u0026nbsp;is more effective for smaller sand particle sizes\u003c/li\u003e\n \u003cli\u003eSmaller particle size of sand lead to higher permeability reduction, higher compressive strength and higher calcium carbonate contents in biosandstone\u003c/li\u003e\n \u003cli\u003eX-ray microcomputed tomography and image analysis is sufficient to characterize the pore system of biosandstone and detect uneven biocemented areas\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUsage of different digital filters influence resulting images and data from X-ray microcomputed tomography\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eMicrobiologically Induced Calcium Carbonate Precipitation (MICP) is a technology that got more and more attention in research during the last years [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Through different microbiological pathways calcium carbonate is precipitated. The most commonly used pathway is still the ureolytic MICP. Ureolytic microorganisms hydrolyse urea to ammonium-ions and carbonate-ions (Eq.\u0026nbsp;1) while the latter will be precipitated in the presence of calcium-ions as calcium carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e) (Eq.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CO{\\left(N{H}_{2}\\right)}_{2}+ {2H}_{2}O\\to C{O}_{3}^{2-}+2 N{H}_{4}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(C{O}_{3}^{2-}+C{a}^{2+}\\to CaC{{O}_{3}}_{\\left(s\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis CaCO\u003csub\u003e3\u003c/sub\u003e can form bridges between particles and act as a binder and can therefore improve mechanical parameters of different soil types. Researchers have shown the potential of MICP to improve compressive strength [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e39\u003c/span\u003e], liquefaction resistance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and resilience against wind erosion [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e] of soils. Furthermore, it can be used to improve compressive strength and reduce water permeability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e] of existing construction materials. Through this process it is also possible to produce novel, artificial sandstone like, materials. Although MICP based materials are not yet ready to be used for construction, they can be used for research purposes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e] opens up a now field of study. Regardless of the intended use case of biocemented soils, the homogeneity of calcium carbonate distribution in the sample is important to evaluate the quality of the cementation. Most studies use cyclic application of a calcination solution (Urea/Calcium salts) and Bacteria suspension (ureolytic microorganisms) to achieve biocementation. This often leads to clogging and inhomogeneous samples. It is possible to estimate sample homogeneity through measurements of calcium carbonate content or compressive strength content in different sections of the biosandstone which has been done several times in literature [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. But these results are limited to sections of consolidated samples and do not yield information about small local inhomogeneities or uniformity from the outer layers of the sample to the core. Since mechanical parameters of biosandstone correlate with the calcium carbonate content in the sample [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e42\u003c/span\u003e], reproduceable results for MICP will therefore largely depend on achieving a homogenous calcium carbonate distribution. Research has described several biological, chemical and physical parameters that impact the final results of MICP [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While a lot of these parameters like type and concentration of bacterial cells, temperature, composition of calcination solution, etc. can be controlled, the influence of the particle size distribution of sand dictates if the locally available sand source is suitable for MICP. Although research on the influence of particle size and shape of sand acknowledges the importance of these parameters on the result of MICP the understanding of these effects remains limited [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Measuring mechanical parameters and calcium carbonate content of biosandstone after MICP can only give limited insight in intrinsic effects that lead to these observed characteristics. It is therefore necessary to improve our understanding of the internal structures and the pore network of biocemented materials and their resulting influence of e.g. unconfined compressive strength (UCS) or water permeability\u003c/p\u003e \u003cp\u003eBy using X-ray microcomputed tomography (micro-CT) it is possible to analyze materials destruction free. Therefore, X-ray images of the samples are taken based on the attenuation of X-rays due to density differences, differences in atomic numbers or phase boundaries. After reconstruction, images with distribution of grey values can be analyzed by 2D and 3D image analysis. This makes it possible to gain detailed insights into the inner structure of these samples. Scientists have analyzed different types of natural sandstone with Xray CT [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e] also MICP was characterized by CT [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e] .The examinations of MICP using CT mainly comprised the sandstone structure, the pore network, the permeability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e], creation of cracks and potential of crack sealing [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e40\u003c/span\u003e], or degree of calcite precipitation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Various procedures for determining the aforementioned parameters based on image analysis are described in literature. There are different digital filters used to process the CT images and get better signal to noise ratios. At first, the denoising principle based on the replacing of color pixels or grey values with the average of nearby pixels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By the use of filters, the grey values are processed e.g. with a normal distribution (Gauss filter) or a median of the grey values in the image window or section (Median filter) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Because pixels with same grey value have no reason to be close at all and therefore, the Non-local Means filter uses the entire image for comparison of each pixel [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Also weighing factors are used at this filter [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor image analysis of CT images of sandstone or MICP sandstone Kirkland et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and Ming et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e] use Gauss filter, while Zhao et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e43\u003c/span\u003e] use a median filter. In contrast, Schl\u0026uuml;ter et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Schmitt et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Thomson et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e], Sun Bing et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e], Peltz et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Su Yang et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e] use the non-local means filter (NLM). Gong Nie et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e] show that the images look very different when Gauss, NLM, anisotropic diffusion or mean filters are used. This research group found the NLM filter to be the most suitable and carried out the image analysis with it. What all these publications have in common is that the results without/with the use of the filter or the use of different filters are not shown in their effects on the image analysis results, but the filters are only considered as part of the image analysis. Filters have an influence of the grey value distribution in images by e.g. shifting the distribution and therefore have an impact of what remains for the subsequent image analysis. This paper gives both, an overview about the visual effects of the applied filters but also the differences in resulting pore network of the MICP sandstone.\u003c/p\u003e \u003cp\u003eIn this work, we present the influence of different grain sizes of sand on calcium carbonate content, water permeability and unconfined compressive strength (UCS). Simultaneously utilizing X-ray microcomputed tomography (micro-CT) to gain insight into the biosandstone and its internal pore network. Additionally, a comparison of different digital filters was performed to evaluate their influence on the quality of the image processing of CT images.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cultivation of \u003cem\u003eSporosarcina pasteurii\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eDuring this study, \u003cem\u003eSporosarcina pasteurii\u003c/em\u003e (ATCC 11859) was used as an ureolytic microorganism for MICP. For cultivation of \u003cem\u003eS. pasteurii\u003c/em\u003e the culture medium for according to ATCC was used. The medium contained 20 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e yeast extract (Carl Roth, Germany), 15.75 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e TRIS-buffer (Carl Roth, Germany) and 10 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ammonium sulfate (VWR International, USA). For media preparation TRIS-Buffer was adjusted to pH 9.2 and then split into two parts: ammonium sulfate was added to one part and yeast extract to the other. The two parts were then separately autoclaved and after cooling combined under sterile conditions. \u003cem\u003eS. pasteurii\u003c/em\u003e was incubated in 500 mL erlenmeyer flasks without baffles with a filling volume of 200 mL at 30\u0026deg;C and at 250 rpm (Multitron S-000115689, Infors HT, Switzerland) until the cells reached late exponential phase. The cells were then harvested and washed three times with 0.9% NaCl. Centrifugation after each washing step was performed at 10\u0026deg;C for ten minutes at 4000 rpm. Cells were then resuspended in 0.9% NaCl (Carl Roth, Germany), to an OD\u003csub\u003e600\u003c/sub\u003e of 3.5 and stored at 4\u0026deg;C until cementation experiments. Additional to OD\u003csub\u003e600\u003c/sub\u003e the ureolytic activity of \u003cem\u003eS. pasteurii\u003c/em\u003e was determined by conductivity method as described by Whiffin [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sand types for cementation experiments\u003c/h2\u003e \u003cp\u003eFor cementation experiments two sands were investigated. SH (Sand Haltern) is a silica sand from Haltern (Quarzwerke Haltern GmbH, Haltern, Germany) that was used in previous studies for MICP [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SP (Sand Picard) is waste sand from a Sandstone Quarry (Carl Picard Natursteinwerk GmbH, Krickenbach, Germany). The particle size distribution (PSD) of 500 g of the sands was determined after wet sieving and drying for 24 h at 105\u0026deg;C with sieves (Diameter 200 mm, RETSCH GmbH, Germany) with mesh size between 0.063 mm and 16.0 mm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each sample is named by the name of the sand type (SH, SP) and the particle size (0063, 0125, 0250, 0500, Mix) for example SP0250 is the fraction with d\u003csub\u003emax\u003c/sub\u003e=0.250 mm from the sand from the Sandstone quarry Picard (SP). Additionally, the particle size distribution (PSD) of SH was replicated from fractions of the Sands SP to compare different sand types with nearly identical PSD. This recreation is called \u0026ldquo;SPMix\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of PSD from the Sand SH, SP, and SP after mixing to achieve the same PSD as SH. \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003emin\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e give the lowest and highest range of particle size that can pass the sieves.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParticle size in mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMass percentage of the particle fraction in %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSH\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSP Mix\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.063\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e15.28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e4.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.125\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1.87\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e6.40\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1.87\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e75.81\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e30.41\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e75.81\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e17.7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e39.31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e17.7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e7.56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e4.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e8.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e16.0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.33\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Preparation of sand columns\u003c/h2\u003e \u003cp\u003eSand columns were prepared in two sizes to meet the different size requirements of the analytical methods. For unconfined compressive strength and water permeability tests sand columns were prepared from 50 mL reaction tubes, diameter 27 mm (Greiner centrifuge tubes, Sigma-Aldrich). The bottom part of the tube was cut off and five holes (diameter 6 mm) were drilled in the cap to allow liquids to flow freely through the column. Prior to filling the columns with sand, the holes were covered with filter paper (40 \u0026micro;m. Qualitativ Filterpapier 417, VWR International). The columns were filled with 40 g sand. The sand was covered with filter paper as well to prevent disturbing the surface of the sand column during MICP. Afterwards, the columns were closed with a plug containing a hole for the application of bacteria suspension and calcination solution during MICP. For X-ray microtomography smaller samples were necessary. Therefore, columns were prepared from the top part of 10 mL standard pasteur pipettes not graded 300 mm (Carl Roth, Germany). They were cut at the top and the bottom part was shortened to 1 cm to allow better handling of the columns. The outlet of the pipette was sealed with cotton. The conical part of the pipette was filled with sand SH [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] as drainage. A non-woven all-purpose cloth (84% viscose, 16% polypropylene, W5, Germany) was placed on this drainage sand. Then 5 g of sand was filled in and the sand was covered with filter paper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 MICP Protocol for biocementation\u003c/h2\u003e \u003cp\u003eMICP treatment was conducted through sequential injection of resuspended cell suspension (OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.5) and a calcination solution containing 1492 mM urea (Fisher Scientific, USA) and 1392 mM calcium chloride dihadrate (Carl Roth, Germany).\u003c/p\u003e \u003cp\u003e Initially, 50% pore volume of cell suspension was applied on the column with a peristaltic pump (IPC 8, ISMATEC, Switzerland) with a flow rate of 3 mL∙min\u003csup\u003e-1\u003c/sup\u003e. After 2 hours 50% pore volume of calcination solution was applied at 3 mL∙min\u003csup\u003e-1\u003c/sup\u003e. This treatment was followed by 5 hours of incubation at 25\u0026deg;C. This treatment is considered as one cycle with 10 cycles in total for each column. 10 Cycles were chosen because preliminary experiments showed that with 10 cycles we can achieve a reasonable strength of the samples while clogging of the samples started to occur from 10 cycles onwards which lead to uneven cementation and a wider variance of compressive strength (Supplement 7). After the last cycle columns were washed with 300% pore volume of distilled water to flush out residual salts and cells and dried for 72 hours at 60\u0026deg;C. After curing at room temperature for 14 days unconfined compressive strength and permeability tests were performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Determination of calcium carbonate content\u003c/h2\u003e \u003cp\u003eFor calcium carbonate determination 5 g of the sample were mixed with 20 mL of 2 M hydrochloric acid. Once no production of CO\u003csub\u003e2\u003c/sub\u003e gas could be visually observed the reaction was given another hour to dissolve any remaining calcium carbonate. Afterward the samples were diluted in 2 mM nitric acid and the content of soluble calcium ions was determined by ion exchange chromatography (n 930 Compact IC Flex, METROHM GmbH \u0026amp; Co. KG). Based on the concentration of soluble calcium ions the total amount of calcium carbonate in the sample was calculated and the content in % calculated as the quotient of calcium carbonate about the initial sample weight.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Image acquisition by X-ray microcomputed tomography\u003c/h2\u003e \u003cp\u003eTo get a better resolution on micro-CT. the smaller samples (diameter about 10 mm) were used as mentioned in Chap.\u0026nbsp;2.3. Depending on the sample diameter, a scan resolution of 6.79 \u0026micro;m/pixel was achieved. With 90 kV and 110 \u0026micro;A as well as a 0.25 mm aluminum filter and an exposure time of 3012 ms the samples were X-rayed at 0.3 \u0026deg; angle steps. Scan duration was about three hours in a skyscan 1272 X-ray microtomograph (Bruker, Kontich, Belgium). To reduce noise, a frame averaging of 4 was applied.\u003c/p\u003e \u003cp\u003eThereafter the reconstruction was performed by using NRecon 2.0.0.5 (Bruker, Kontich, Belgium). Here postalignment was 1, no smoothing was applied, ring artefacts reduction was set to 50 although there were less to none visible. Min and max image conversion were set to 0 resp. 0.077945. Images were reconstructed as tiff 16 images.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of micro-CT samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticle Size in mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight in g\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalculated object height in Volume of Interest in mm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;0.063\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e10.054\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;0.125\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e10.570\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;0.250\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e10.183\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;0.500\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e10.257\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Determination of water permeability\u003c/h2\u003e \u003cp\u003eWater permeability was determined as the coefficient of permeability \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{\\text{f}}\\)\u003c/span\u003e\u003c/span\u003e by Darcy\u0026acute;s law. The measurement was conducted at test station compliant with DIN EN ISO 17892-11 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The test station includes a pressure-controlled tank made of polyvinyl chloride. The pressure in the pressure tank is set to a constant value of p\u0026thinsp;=\u0026thinsp;0.5 bar by a compressor, which is regulated by a pressure gauge. The container is a tube that is screwed to a teflon top and bottom section using threaded rods. The samples are measured using modified centrifuge tubes. These are positioned in the centre of the pressure vessel. Filter disks made of sintered bronze with a layer thickness of 2 mm are placed above and below the sample or the loose bulk material. The centrifuge tube is screwed into a thread in the base section. The upper opening of the tube is closed using a silicone plug in which a silicone tube is positioned. The flow rate of water is set manually in the range 2\u0026ndash;200 mL\u0026middot;min\u003csup\u003e-1\u003c/sup\u003e. Once a stationary flow regime through the sample has been established, the amount of liquid passed per time can be measured. The measurement duration of the liquid volume was five minutes with one measurement being carried out every minute. The quantity of liquid that passes through is collected in a container and the mass was determined using a digital scale. The coefficient of permeability \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{\\text{f}}\\)\u003c/span\u003e\u003c/span\u003e was calculated with the equations of Darcy\u0026acute;s law (Eq.\u0026nbsp;3\u0026ndash;5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Delta }{h}_{\\text{W}}=i\\cdot {H}_{\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(v= \\dot{V}\\cdot {A}^{-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{\\text{f}}=v\\cdot {i}^{-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq.\u0026nbsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the measured sample height \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e and a self-selected hydraulic gradient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e, the hydraulic pressure difference \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Delta }{h}_{\\text{W}}\\)\u003c/span\u003e\u003c/span\u003e can be calculated (Eq.\u0026nbsp;3). This is required to set the pressure applied to the pressure gauge. The flow velocity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(v\\)\u003c/span\u003e\u003c/span\u003e is then calculated using the volume flow \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{V}\\)\u003c/span\u003e\u003c/span\u003e, which is determined by the individual measurements, and the cross-sectional area \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A\\)\u003c/span\u003e\u003c/span\u003e of the biosandstone sample (see Eq.\u0026nbsp;4). Finally, the permeability coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{\\text{f}}\\)\u003c/span\u003e\u003c/span\u003e is determined using the flow velocity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(v\\)\u003c/span\u003e\u003c/span\u003e and the hydraulic gradient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e (see Eq.\u0026nbsp;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Determination of unconfined compressive strength\u003c/h2\u003e \u003cp\u003eUnconfined compressive strength (UCS) was determined with a universal testing stage (Compression Testing Machine Type 812, FHF Strassentest, Germany). Before measurement the top of samples were sanded flat to achieve an even surface for force application during measurement. The final height of the samples \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e were 40\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mm. Force was applied with a traverse speed of 2 mm\u0026middot;min\u003csup\u003e-1\u003c/sup\u003e until the sample broke. From the resulting stress-strain diagrams the UCS was determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Image analysis\u003c/h2\u003e \u003cp\u003eFirst of all, the Volume of Interest (VOI) is determined. Due to the sample setup inside the pipette with the loose sand or MICP sandstone between filter paper and non-woven cloth, the amount of images in one stack varies. The separation objects filter paper or non-woven cloth can lie in an angle and therefore, the images for image analysis have to be chosen wisely. For the analysis, only images without pipette walls, filter paper or non-woven cloth are used. Despite efforts to keep deviations as small as possible, the mean of images in one stack is about 1511\u0026thinsp;\u0026plusmn;\u0026thinsp;28 images. This results in an object height of 10.266\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 mm for MICP sandstone characterized with micro-CT. After the VOI determination, the image analysis can be started. Therefore, either no filter, Gauss filter or Non-local means filter were applied. No filter gives the reference, while gauss filter is quite popular and part of many image analysis software. So it is a good option to compare it to other digital filter results. And non-local means filter was chosen because of the fact, it uses the entire image not just surrounding pixels for comparison of grey values and their adjustment.\u003c/p\u003e \u003cp\u003eThe image analysis was performed with CTAnalyser V1.20 (Bruker. Kontrich, Belgium). Gauss filtering is possible with CTAnalyser (CTAn), while Non-Local Means (NLM) has to be performed with Fiji, an open source image processing package basing on ImageJ2. In Fiji it is possible to use the Non-local means plug-in based on the work of Buades et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and Darbon et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. NLM was performed with Sigma\u0026thinsp;=\u0026thinsp;15. Afterward, a lot of noise, especially in sand grains, is removed. But it has to be kept in mind, that filters maybe cause artefacts, which are then part of the image analysis and results. For the image analysis with CTAn two task lists are necessary. In the first task list, sandstone volume and porosity of the object are calculated. Afterwards a Region of Interest (ROI) shrink wrap is performed which finishes the first task list. Here, a new region of interest can be created from an existing ROI by adapting the shape of the sandstone object, which was calculated before [4]. The second task list is all about analysing pore space between sand grains. Of interest here are either connected pores, which form a coherent pore system, or the individual pores. By using bitwise and morphological operations it is possible to fully separate the connected from the individual pore system. For the connected pores a 3D-analysis was performed and the individual pores were analysed by individual object analysis. Both task lists can be found in supplementary material (Supplement 8\u0026ndash;11)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Relationship between Calcium carbonate content, Water Permeability, and Unconfined Compressive Strength of biosandstone\u003c/h2\u003e \u003cp\u003eThe treatment of each sand with MICP resulted in observable calcium carbonate depositions on the sand surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the samples, effective calcium carbonate bridges as well as ineffective crystals that grew into void space could be observed. The former effect is the main goal of biocementation through MICP for the improvement of mechanical soil characteristics and the reason for the observed strength of sand after biocementation. The observed quantities of calcium carbonate range from 11.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25% g g\u003csup\u003e-1\u003c/sup\u003e to 6.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59% g g\u003csup\u003e-1\u003c/sup\u003e of total weight with an overall trend of decreasing calcium carbonate content with increasing particle size (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor each sample, there is a decrease in water permeability caused by MICP treatment. For the loose sand and samples treated with MICP, the water permeability is inversely proportional to the grain size (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The water permeability coefficient for the loose sand fraction SP0063 \u0026ndash; SP0500 ranges from 6.37∙10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e to 4.55∙10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e and decreases through MICP treatment to 2.12∙10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e (SP0063) to 2.50∙10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (SP0500). The reduction of permeability ranges therefore from 96.7% (SP0063) to 45.0% (SP0500) and generally decreases with increasing particle size. The strongest decrease from 6.36∙10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e to 2.12∙10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e occurs for the smallest fraction SP0063. In contrast, the lowest decrease from 4.55∙10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e to 2.50∙10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e m∙s\u003csup\u003e-1\u003c/sup\u003e occurs for the largest fraction SP0500. Permeability coefficients of SH and SPMix lie between SP0125 and SP0250, which is because these fractions make up the largest proportion of this fill at 75.8% and 17.7%. Both the two sands SH and SPMix as well as the individual fractions of SP show a reduction in water permeability after MICP treatment of 45.0 to 96.7%. This reduction of permeability through MICP can be attributed to the blocking of flow paths through the sand. Especially large pore networks and channels through the sand get blocked by cementation and permeability is reduced with increasing degree of cementation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The higher decrease for lower particle sizes is most likely attributed to this blocking effect. For smaller particles void space decreases which could lead to calcium carbonate crystals forming with a higher chance at pore throats leading to reduced permeability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, for biosandstones with smaller particle size a generally higher degree of cementation was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) which also increases the chance of pore blocking.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUCS shows a similar trend for sand fractions. With increasing particle size the UCS of the biocemented samples decreased from 3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 MPa (SP0063) to 0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 MPa (SP0500) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Since the void space between particles is bigger for higher particle sizes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e] the formed calcium carbonate crystals have a higher chance of growing into voids instead of forming effective bridges at contact points. SP0063 and SP0125 have nearly identical UCS with SP0125 with higher variances than samples with a bigger particle size which might be due to a higher chance of clogging in the sample which leads to a more uneven distribution of bacteria suspension and calcination solution and therefore uneven calcium carbonate distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis clogging effect is a common phenomenon of MICP calcinated sand although more prominent in sand with low permeability since bacteria suspension and calcination solution can distribute more freely through the sand and therefore the reaction is more evenly distributed throughout the samples [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This clogging effect is also likely the reason for the higher decrease in water permeability for smaller particle sizes as the precipitated calcium carbonate blocks pores and thereby flows paths through the sand. The decrease in calcium carbonate content with increasing particle size supports this. Samples with lower particle sizes withhold more calcium carbonate on throat points than samples with bigger particle sizes most likely due to flushing out calcium carbonate from big pores that could not attach to the surface of particles. This effect resulted in higher calcium carbonate content which subsequently resulted in higher UCS and greater reduction of permeability. It is notable, that by reconstructing the particle size distribution of SH with SP did not lead to similar UCS or reduction of permeability coefficient. With 3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 MPa and a reduction of permeability of 51.31% SPMix had a significantly higher UCS compared to SH (1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 MPa) while having a lower reduction in permeability coefficient (71.60%). This suggests that it is not solely the particle size of the sand that impacts the result but also the type of sand. Particle shape and size might be of similar importance. A study conducted by Song et al. investigated MICP-treated silica sand with different particle morphologies and gradings from three sand types sieved into four size fractions: (1.00\u0026ndash;0.85 mm), (0.85\u0026ndash;0.425 mm), (0.425\u0026ndash;0.250 mm), (0.250\u0026ndash;0.180 mm) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. They observed that UCS of treated spherical and near-spherical sands peaked at 5.21 MPa for particle sizes of 0.85\u0026thinsp;\u0026minus;\u0026thinsp;0.425 mm, whereas treated angular sands had increasing UCS with decreasing particle size. Similar to our findings, they observed an increase of calcium carbonate content with decreasing particle size for all tested sand types. They concluded that particle morphology and resultant bonding mechanisms exert critical controls in MICP-grouting that significantly affect the cementation structure and, as a result, the ensemble strength of the treated assemblage.\u003c/p\u003e \u003cp\u003eGowthaman et al. consolidated three well-graded sands Mizunami (d\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.6 mm), Mikawa (d\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.87 mm), and Toyoura (d\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.2 mm), and achieved compressive strengths of 1.82 MPa. 2.67 MPa and 3.98 MPa respectively [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Zhao et al. observed a notable reduction in permeability and improvement in UCS with the incorporation of different fine particle contents and pore ratios in silt [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Although our understanding of the mechanisms behind the impact of grain size and shape of particles on the process remains limited [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the importance of particle size on UCS, permeability reduction, and calcium carbonate content of biocemented sand when choosing sand for MICP is clear. In summary, our research findings are in alignment with recent literature, indicating that the efficacy of MICP in improving UCS, reducing water permeability, and influencing calcium carbonate content is significantly affected by sand particle sizes. These insights not only validate our research outcomes but also highlight the necessity of precise knowledge of the soil that will be utilized for MICP. Although the macroscopic measurements of UCS, calcium carbonate content, and permeability hold valuable information on the efficiency of the MICP process it remains important to gather information on the internal structure of biocemented sand that cannot be explained by these measurements. CT image analysis is a tool that could help extend our understanding of the internals of biocemented sand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 CT results\u003c/h2\u003e \u003cp\u003eImages of biosandstone, created by X-ray microcomputed tomography and image analysis, were first of all processed with or without a digital filter. Afterwards, image analysis was performed with the two task lists (Supplement 8\u0026ndash;11). Resulting images can be separated by their containing objects like sandstone, connected pore system or individual pores. A comparison of image components like sandstone, pore network, or edge areas shows: Whether or not a filter is used, the sandstone has an average proportion of about 63% of the image, while the pores have about 13.5\u0026ndash;14.9% as can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The rest are edge areas, which exist due to the round sample geometry.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImages treated with Non-local means filter show in mean the highest proportion of sandstone and the lowest proportion of pore system. Without a filter, the largest proportion of pores and the smallest proportion of sandstone are present. Noise is probably still present here, which is calculated as pores after setting the threshold and analyzing the image. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of image analysis regarding the volumes of either connected pores or biosandstone, also in comparison with used filters. It can be seen that SP0063 has always the lowest volume of connected pores. The reason for this may be that the small sand grains are more densely packed than the sand grains in SP0500. The object volume of biosandstone ranges between 1963 mm\u003csup\u003e3\u003c/sup\u003e up to 2068 mm\u003csup\u003e3\u003c/sup\u003e. Differences in the volume of biosandstone regarding the used filters can be recognized.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVolumes of connected pores and biosandstone objects calculated by CT and image analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWithout filter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGauss filter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNLM filter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eConnected Pore Volume in mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSandstone object volume in mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eConnected Pore Volume in mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSandstone object volume in mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eConnected Pore Volume in mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSandstone object volume in mm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e428\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1974\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e446\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1963\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e418\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1981\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e466\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2061\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e466\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e2068\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e462\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2068\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e468\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1967\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e452\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1990\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e453\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1998\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e469\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1974\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e453\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e1999\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e440\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2010\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen analyzing the pore system, it must be noted that there are two ways to achieve the results: CTAn task list 1 calculates the porosity based on the sandstone object, while there is also the possibility to characterize the pore system directly to get more information. This is done by the second task list, as described in Chap.\u0026nbsp;2.9. The detection limit for pores due to the resolution must be taken into account.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows excerpts of the results of image analysis either of sandstone or pore network. The direct characterization of the pore network shows that there is a large network of connected pores. It can be seen that the individual pores are found more frequently in the edge areas towards the pipette wall at every used sand grain fraction. Whereas the connected pores are mainly at the center of the biosandstone. This is particularly interesting about a uniform flow of both, bacteria solution and calcination solution and has also an effect on solidification. Because the solutions should distribute homogeneously through the sand and not just pass through completely. To achieve this, the reaction time must be correct so that there is sufficient time for the cementing reaction. In addition, the cementation should be as uniform as possible through the sample to achieve reproducibility. The micro-CT images provide insight in the biosandstone and enable the detection of optimization potentials. The general visualization of biosandstone can significantly enhance our understanding of the cementation process. Notably, the edges of the biosandstone exhibit stronger calcination compared to the internal part of the sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This observation hints at a potential flushing-out the effect of residue bacteria cells and small calcium carbonate crystals with each new cycle of MICP treatment. Micro-CT measurements offer valuable insight and enable a deeper investigation into the differences between various cementation protocols. For future research, it could be interesting to investigate for example the effects of the contact flexible mold on the distribution of calcium carbonate crystals in the biosandstone [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. During this cementation protocol, the sand is loaded with bacteria suspension and the sample is immersed in a bath of calcination solution. Micro-CT could help to detect how strongly pronounced a cementation gradient from the outer layers to the internal parts of the biosandstone is. Also, it helps to detect defects and large pore systems. These large pore systems can form channels and have a bigger impact on the permeability of sandstone than just the porosity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Also, possible weak points in the sandstone in the form of large pore systems that impact the UCS of biosandstone can be detected by micro-CT. Although it was not possible for us to find a correlation between large pore systems and breaking points of Biosandstone due to the destructive nature of UCS testing, a coupled measurement of UCS and CT scans could be possible to detect initial crack forming. The location of these cracks in the biosandstone would help greatly to improve our understanding of cementation protocols. Whereas the individual pores have also an influence on the biosandstone strength, their influence depends on the pore volume and their number. If there are lots of big pores, the biosandstone stability of course is reduced. Examination of the individual pores shows differences in the number of pores depending on the used filter (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Here, it can be seen that without a filter a lot of noise is recognized as a pore network, so the use of a sufficient filter is quite useful. The resolution of the images should also be taken into account, especially with small sand grain sizes. Because it is more difficult to distinguish whether there are pores or noise is changing the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of individual pores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout filter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGauss filter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNLM filter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNumber of individual pores\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNumber of individual pores\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNumber of individual pores\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e581 020\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e456 481\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e468 219\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e398 861\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e277 628\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e305 105\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e380 962\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e171 691\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e142 371\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSP0500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e420 214\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e136 188\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e100 691\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the individual pores as pore volume distributions. There it can be seen, especially for SP0063 and SP0125, that without the use of a filter, the distribution is shifted towards small pore diameters. Additionally, with consideration of the other curve profiles, which show fewer small pores, it can be estimated, that without a filter there is noise in the ROI images, especially for SP0125. It is also noticeable in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e that with increasing sand grain size the amount of pores towards higher pore diameters increases. SP0250 and SP0500 show higher pore diameters differences about the used filter. Based on this data, no trends can be seen but to be sure, a higher sample size has to be analyzed. Also, it has to be kept in mind that pore volumina are calculated as spheres and the influence of the pore diameter is high. Therefore, small differences in the filtering process can lead to bigger pores and are shown as peaks in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. While the pores of biosandstone are mostly not in the shape of a sphere due to the shape of sand grains and their packing, the assumption of a spherical shape is a simplification to create a working model. The representation as pore distribution curves provides good possibilities for comparison over the entire sample height. In contrast, Minto et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]chose to display the porosity per image along the z-axis to provide an insight into the pore structure in the biosandstone.\u003c/p\u003e \u003cp\u003eIn SP0063 there is, regardless of whether a filter was used in the image analysis, the highest proportion of individual pores with about 8.76% (NLM filter) up to 10% (without filter or Gauss filter) of total pore volume. Towards larger sand grains, the proportions become smaller and smaller until it is only 1.39% (NLM filter), 1.48% (Gauss filter), or 2.87% (without filter) for SP0500.\u003c/p\u003e \u003cp\u003eIf the entire pore system is considered, it can be seen that the volume of connected pores is lower after using the NLM filter than without the filter (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), despite otherwise identical treatment of the images. This illustrates the effect of the NLM filter very clearly, as the gray values are averaged here as described above. Some areas that would be counted as pores without the filter are probably influenced by NLM in such a way that they are included at a threshold of 50\u0026ndash;255 and counted as sandstone. The use of filters in image analysis and characterization of pore systems has an impact on the results which is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The influence of the filter method varies for different analysis results like the amount of individual pores, volume of connected pores, or total porosity as well as sandstone volume. It is important to mention the filtering method in investigations and consider its effects on calculations. Filters are necessary to reduce visible noise in micro-CT images. The Gauss filter visually reduces noise by smoothing but still retains a high proportion of very fine pores and speckles. On the other hand, the NLM filter performs well, particularly for fractions with larger sand grains. However, it should be noted that the NLM filter produces a noticeable difference from the original image due to the way it changes the gray values, which affects the results when setting the threshold for image analysis.\u003c/p\u003e \u003cp\u003eAll in all, the use of a filter for our data is necessary to remove noise. The comparison of gauss filter and non-local means filter showed differences in resulting images. Thresholding of this images led to different amounts of sandstone volume or pore network volume. By using the non-local means filter, a more uniform grey value distribution within the sand grains could be achieved and therefore less speckles within image analysis. It has to be kept in mind, that the sigma parameter must be adjusted to the size of the sand grains depicted so that even the smallest grains of sand and pores are still sharply depicted and are not removed by blurring, if sigma is too large. We recommend using the NLM filter for the application on biosandstone, taking into account the problems and limitations mentioned above.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eIn this study, different sand fractions from a local sandstone quarry were consolidated using MICP. The resulting biosandstones were analyzed for unconfined compressive strength, water permeability, and calcium carbonate content. Additionally, the samples were visualized by micro-CT, and the resulting pore system was analyzed. From the results, the following conclusions can be drawn:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSand fractions ranging from 63 \u0026micro;m to 500 \u0026micro;m as well as ungraded sands could effectively be treated with MICP which resulted in a reduction of permeability coefficient and measurable unconfined compressive strengths (UCS) of biosandstone\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSmaller particle size of sand was better suited for MICP than larger particle size. For smaller sand particles higher UCS, calcium carbonate content and reduction of permeability coefficient could be observed\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eParticle size distribution is not the only parameter of sand affecting the efficiency of MICP. Particle shape likely impacts the MICP as well which became apparent by the different results of industrial sand and a reconstruction of the particle size distribution of industrial sand with local quarry sand\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhen using locally available sand resources it is necessary to investigate particle size distribution before MICP treatment and eventually reconstruct a more suitable particle size distribution by recombining sand fractions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eX-Ray microcomputed tomography (micro-CT) is suitable for visualization of biosandstone and its pore space but appears to be limited for small particle sizes due to difficulties in differentiating between pore space and noise for certain resolutions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe choice of filter is important to consider for processing the micro-CT data. Without filtering, noise is apparent in the particle size distribution curves and different filtering changes the results of the observed pore space and porosity\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInhomogeneous areas of biosandstone can be visualized destruction free by micro-CT. This could help to characterize homogeneity of biosandstone in addition to segmented calcium carbonate measurements. In future studies, it could be interesting to investigate if these areas act as breaking points by combining compressive tests with micro-CT measurements\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was financially supported by the Deutsche Forschungsgemeinschaft (DFG. German Research Foundation) \u0026ndash; Project-ID 172116086 \u0026ndash; SFB 926 and the \u0026ldquo;Landespotentialbereich NanoKat\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Martin Picard and the staff of Carl Picard Natursteinwerk GmbH for providing the sand necessary to conduct this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would also like to thank Prof. Dr. Christos Vrettos for the opportunity to use the equipment at the department for Soil Mechanics and Foundation Engineering and for his expert input on several occasions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.S. and N.E.conceived and,planned the experiments. Material preparation, data collection, and analysis were performed by S.S, N.E. and T.S.. The manuscript was written by S.S. and N.E. equally. All authors provided critical feedback and input for the manuscript. \u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkimana RM, Seo Y, Li L et al. (2016) Exploring X-Ray Computed Tomography Characterization and Reactive Transport Modelling of Microbially-Induced Calcite Precipitation in Sandy Soils. American Society of Civil Engineers\u003c/li\u003e\n\u003cli\u003eAl Qabany A, Soga K, Santamarina C (2012) Factors Affecting Efficiency of Microbially Induced Calcite Precipitation. 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Applied Sciences 12:139. doi: 10.3390/app12010139\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-materials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dime","sideBox":"Learn more about [Discover Materials](https://www.springer.com/journal/43939)","snPcode":"","submissionUrl":"","title":"Discover Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microbiologically Induced Calcium Carbonate Precipitation (MICP), Sporosarcina pasteurii, X-ray microcomputed tomography (micro-CT), image analysis, Gauss filter, Non-local Means filter, pore network","lastPublishedDoi":"10.21203/rs.3.rs-4489051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4489051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicrobiologically Induced Calcium Carbonate Precipitation (MICP) is a technology for improving soil characteristics, especially strength, that has been gaining increasing interest in literature during the last few years. Although a lot of influencing factors on the result of MICP are known, particle size and shape of the particles remain poorly understood. While destructive measuring of compressive strength or calcium carbonate content are important for the characterization of samples these methods give no insight into the internal structures and pore networks of the samples. X-ray microcomputed tomography (micro-CT) is a technique that is used to characterize the internals of rocks and to a certain degree MICP-treated soils. However, the impact of filtering and image processing of micro-CT Data depending on the type of MICP sample is poorly described in the literature. In this study, single fractions of local quarry were treated with MICP through the ureolytic microorganism \u003cem\u003eSporosarcina pasteurii\u003c/em\u003e to investigate the influence of particle size distribution on calcium carbonate content, unconfined compressive strength and the reduction of water permeability. Additionally, micro-CT was conducted to obtain insights into the resulting pore system. The impact of the Gauss filter und Non-local means filter on the resulting images and data on the pore network are discussed. The results show that particle size has a significant impact on the result of all tested parameters of biosandstone with lower particle size leading to higher strength and generally higher calcium carbonate content. Micro-CT data showed that the technology is feasible to gain valuable insights into the internal structures of biosandstone but the resolution and signal-to-noise ratio remain challenging, especially for samples with particle sizes smaller than 125 \u0026micro;m..\u003c/p\u003e","manuscriptTitle":"MICP treated sand: Insights into the impact of particle size on mechanical parameters and pore network after biocementation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 18:50:21","doi":"10.21203/rs.3.rs-4489051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-26T17:17:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-25T15:39:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-23T10:08:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53775642858424554321940588036120065844","date":"2024-06-21T14:29:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73738268260958890651208260922883724121","date":"2024-06-19T04:43:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279670390110132444815058757614947204478","date":"2024-06-18T22:14:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-18T21:51:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-04T18:29:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-04T18:25:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Materials","date":"2024-05-28T07:49:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-materials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dime","sideBox":"Learn more about [Discover Materials](https://www.springer.com/journal/43939)","snPcode":"","submissionUrl":"","title":"Discover Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e55a7a15-d523-4734-b603-a71e657ff10e","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T16:01:57+00:00","versionOfRecord":{"articleIdentity":"rs-4489051","link":"https://doi.org/10.1007/s43939-024-00108-3","journal":{"identity":"discover-materials","isVorOnly":false,"title":"Discover Materials"},"publishedOn":"2024-09-20 15:57:28","publishedOnDateReadable":"September 20th, 2024"},"versionCreatedAt":"2024-06-20 18:50:21","video":"","vorDoi":"10.1007/s43939-024-00108-3","vorDoiUrl":"https://doi.org/10.1007/s43939-024-00108-3","workflowStages":[]},"version":"v1","identity":"rs-4489051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4489051","identity":"rs-4489051","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}