From Photosynthesis to Antioxidants: How Silicon (K₂Si₂O₅) Improves Yield and Grain Quality in Rice

From Photosynthesis to Antioxidants: How Silicon (K₂Si₂O₅) Improves Yield and Grain Quality in Rice | 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 From Photosynthesis to Antioxidants: How Silicon (K₂Si₂O₅) Improves Yield and Grain Quality in Rice Jinghua Xu, Ya Zhan, MIN Xie, Weiwei Geng, Liu Ao, Can Guo, Shang Gao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7606764/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Silicon is increasingly recognized as a beneficial element for rice growth, yet limited research has explored how it regulates photosynthesis to influence yield and quality. Methods Through pot experiments and three years of field validation, this study systematically investigated the effects of different silicon concentrations on photosynthetic characteristics throughout the entire growth cycle of rice, as well as on yield and quality parameters. Results The results show that silicon application significantly increased the net photosynthetic rate of leaves at all growth stages and optimized photosynthetic parameters (elevated Fv/Fm, Fv'/Fm', qP, Y(II), and ETR; reduced NPQ). It also enhanced photosynthetic pigment content, improved photosynthase activity and membrane integrity. Additionally, silicon activated the antioxidant defense system, boosting the activity of antioxidant enzymes (CAT and SOD) and stimulating the ASA-GSH cycle, thereby comprehensively enhancing antioxidant capacity. Under field conditions, silicon application significantly increased grain yield and biomass yield while improving quality metrics: reduced chalkiness, optimized starch content and composition, and enhanced processing quality and nutritional value. Notably, silicon treatment increased the content of key aromatic compounds (particularly 2-AP), leading to an overall improvement in quality. These findings indicate that silicon improves yield and quality by enhancing photosynthetic efficiency and strengthening the antioxidant system, with the most pronounced effects observed at 0.75 mM pure silicon (applied as H 2 Si 2 O 5 ). Conclusions The study suggests that strategic silicon application can be an effective approach to ensuring food security and promoting sustainable development in the rice industry. Silicon Photosynthesis Rice Quality yield: Antioxidation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Rice ( Oryza sativa L. ) serves as a critical global food staple, constituting the primary dietary energy source for over 25% of the world’s population. While current production levels satisfy global caloric demand, the agricultural research paradigm is undergoing a strategic shift—from yield maximization to quality-driven cultivation (Mathan et al., 2025 ). This transition reflects evolving consumer demands for superior nutritional profiles, sensory attributes, and functional properties in rice (Hori et al., 2025 ). As the most essential biochemical process on Earth, photosynthesis serves as the primary energy conversion mechanism that sustains virtually all terrestrial life, with particular significance in agricultural systems, where it fundamentally determines both crop yield and quality (Eckardt et al., 2024 ). Extensive research has established a robust physiological linkage between leaf photosynthetic performance and seed quality parameters, demonstrating that enhanced photosynthetic capacity serves as a pivotal strategy for simultaneously improving productivity and nutritional value (Shao et al., 2024 ; Dong et al., 2025 ). Pioneering studies by Sun et al. ( 2024 ) revealed that optimizing photosynthesis in millet ( Panicum miliaceum L ) significantly increased grain soluble solids (by 23%) and total phenolic content (by 18%), while Zhao et al., ( 2024 ) showed that heightened photosynthetic activity in sorghum ( Sorghum bicolor ) enhanced both biomass accumulation and key quality traits. These findings have been consistently validated across major cereal crops, including wheat ( Triticum aestivum ), where improved photosynthesis correlates with higher protein content and baking quality (Xu et al., 2025 ); barley ( Hordeum vulgare ) (Gao et al., 2024 ), which exhibits increased β-glucan levels; and maize ( Zea mays ), where photosynthetic optimization boosts both starch content and carotenoid concentrations (Teng et al., 2025). This photosynthesis-quality nexus operates through multiple pathways, including carbon allocation dynamics that enhance grain filling, increased synthesis of secondary metabolites such as phenolics and flavonoids, and improved stress mitigation through enhanced antioxidant capacity (Tong et al., 2024 ). The collective evidence underscores that strategic enhancement of photosynthetic efficiency—whether through breeding, agronomic practices, or biotechnological approaches—represents a transformative solution for addressing both global productivity and nutritional security challenges in sustainable agriculture. Silicon (Si) has emerged as a functionally significant beneficial element for higher plants, with particularly pronounced growth-enhancing and stress-mitigating properties observed in monocotyledonous species such as rice, sugarcane ( Saccharum officinarum ) (Chen et al., 2025 ), and bamboo ( Bambusoideae ) (Guo et al., 2024 ). While not currently recognized as an essential micronutrient, extensive phytophysiological research has unequivocally established its critical role in plant-environment interactions within agricultural ecosystems. Cutting-edge studies utilizing advanced microscopy techniques have revealed that silicon undergoes specific biomineralization processes in plant tissues, forming specialized phytolithic structures within epidermal cell walls (Zhou et al., 2024 ). This unique biosilicification mechanism confers remarkable biomechanical advantages, including enhanced culm strength (increasing stem rigidity by 30–50%), improved resistance to lodging, and optimized canopy architecture through promoted vertical growth orientation (Mullangie et al., 2024 ). Beyond its structural functions, silicon exhibits sophisticated dual-phase defense modulation: it establishes physical barriers through silica deposition while simultaneously priming systemic resistance pathways, thereby providing comprehensive protection against diverse biotic stressors (Zhu et al., 2024 ; Shi et al., 2024 ). Of particular agricultural relevance is silicon's multifaceted role in photosynthetic optimization and abiotic stress amelioration. Current research demonstrates that silicon supplementation can enhance photosynthetic quantum yield by 15–20% through improved light interception efficiency (Xu et al., 2024 ). Furthermore, it orchestrates a sophisticated antioxidant network, upregulating key enzymes including superoxide dismutase (SOD) and catalase (CAT), which collectively enhance plant resilience to environmental challenges (Pang et al., 2024 ). These scientifically validated benefits position silicon as an invaluable component in sustainable crop management systems and precision agriculture applications. Hasan et al. (2024) showed that nano-sized silicon can overcome soil acidification, adjust continuous cropping obstacles, improve photosynthetic efficiency of capsella bursa seedlings, and enhance the function of non-enzymatic antioxidant system of plants. Nowakowska et al. ( 2024 ) have also shown that silicon can improve the photosynthetic efficiency of European beech trees under drought conditions, thereby improving their growth. Similar studies have been done on cherry tomatoes ( Lycopersicon esculentum var. cerasiforme A.Gray ) (Kobayakawa et al., 2023 ), kale ( Brassica oleracea var. acephala DC. ) (Tong et al., 2024 ) and citrus ( Citrus reticulata Blanco ) (jhazmat et al., 2024). However, up to now, there have been few reports on the mechanism of regulating photosynthesis and antioxidant capacity of rice, thus regulating rice quality and yield. It is important to study the effect of silicon on rice yield and quality for the optimization of rice production. Therefore, in this study, rice was treated with different concentrations of K 2 Si 2 O 5 , and photosynthetic related indexes of rice were measured at each stage to reveal the pre-related mechanism of silicon regulation of rice photosynthesis, explore its antioxidant properties at the same time, and finally evaluate the quality of rice, in order to fully reveal the silicon-mediated photosynthesis and antioxidant system of rice. Then the physiological mechanism of rice quality was regulated, and the theoretical basis was provided for increasing rice yield and improving rice quality and ensuring food security. 2 Materials and methods 2.1 Plant material, growth conditions, and exogenous treatments This study employed two commercially important hybrid rice cultivars: Quanyouefengsimiao (QY) and Xinliangyou-223 (XLY), both with a 135-day growth cycle. The pot experiment was conducted at Yangtze University's experimental station in Jingzhou, China (30°32'6"N, 112°12'3"E) during the 2022–2024 growing season (June-November), all the data in this experiment were analyzed based on the average values obtained from the three-year experiment.. Following standard protocols, seeds were surface-sterilized and soaked in distilled water at 25 ± 1℃ for 24 h, then germinated at 30℃ in darkness. Uniformly germinated seeds were selected and transplanted into plastic nursery pots containing characterized growth substrate ( Table S1 ). After 25 days of seedling establishment, plants were transplanted into 38×38×41 cm (L×W×H) cultivation pots, each containing 8 kg of growth medium amended with 8 g of compound fertilizer (N ≥ 16%, P ≥ 9%, K ≥ 15%). The experimental setup consisted of three planting holes per pot with two seedlings per hole. All pots were maintained in a controlled-environment greenhouse with diurnal temperature regulation (25℃ night/30℃ day), 60% relative humidity, and natural photoperiod. Soil moisture was maintained at field capacity through bi-daily irrigation with distilled water. This study implemented five selenium (Si) treatment levels (0 [Control], 0.25 [Si-1], 0.5 [Si-2], 0.75 [Si-3], 1 [Si-4] and 1.25 [Si-5] mM pure silicon) applied as K 2 Si 2 O 5 through foliar spraying during three critical growth phases: early tillering, flowering, and one week post-flowering. Photosynthetic parameters were systematically measured at six developmental stages (tillering, jointing, heading, milk ripening, wax ripening, and full maturity), while yield components (panicle number, grain weight, filled grain percentage). The experiment followed a completely randomized design with three biological replicates per treatment, totaling 30 experimental units, with all measurements conducted using standardized protocols and properly calibrated instruments. Field validation was conducted under standard field cultivation conditions, with the experimental layout illustrated in the accompanying Figure S1 . Seedling cultivation followed identical protocols to the pot experiments. The experimental design comprised 36 plots arranged in three complete replications. At maturity stage, comprehensive assessments were performed including both yield and grain quality parameters. 2.2 Determination of photosynthetic parameters of plant leaves Photosynthetic physiological parameters were measured using a Li-6400 portable photosynthesis system (LI-COR Inc., Lincoln, NE, USA) on clear mornings between 9:00 and 11:00 AM, with measurements performed on fully expanded trifoliate leaves during tillering and jointing stages and flag leaves during subsequent growth stages to determine net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and stomatal limitation value (Ls). Chlorophyll fluorescence parameters, including actual quantum yield of PSII [Y(II)], maximum quantum yield of PSII (Fv/Fm), effective quantum yield of PSII (Fv'/Fm'), electron transport rate (ETR), non-photochemical quenching (NPQ), and photochemical quenching coefficient (qP), were analyzed using a PAM-2500 portable pulse-modulated fluorometer (Heinz Walz GmbH, Effeltrich, Germany) after 2 h of dark adaptation. Additionally, photosynthetic pigment concentrations (Chla, Chlb, and Car) were determined spectrophotometrically according to Zhu et al. ( 2021 ) by measuring absorbance at 665, 649, and 470 nm, while Rubisco (EC 4.1.1.39) activity was assayed using a commercial kit (Solarbio Science & Technology, Beijing, China), with enzyme activity defined as the amount required to oxidize 1 nmol NADH per minute per gram fresh weight at 25℃ (A340), and malondialdehyde (MDA) content was quantified following Zhu et al. ( 2021 ) with spectrophotometric measurements at 450, 532, and 600 nm. 2.3 Determination of antioxidant content The contents of ascorbic acid (ASA), dehydroascorbic acid (DHA), hydrogen peroxide (H 2 O 2 ), and reduced glutathione (GSH) were determined spectrophotometrically using the following procedures: Fresh leaf tissue (0.5 g) was homogenized in 5 mL ice-cold 50 mM phosphate buffer (pH 7.8) and centrifuged (12,000×g, 15 min, 4°C), with the supernatant collected for analysis. For ASA/DHA determination, 0.5 mL supernatant was mixed with 0.5 mL 10% TCA and divided: total ascorbate (ASA + DHA) was measured by adding 0.2 mL 0.4 M phosphate buffer (pH 7.4) and 0.2 mL 10 mM DTT (37°C, 15 min), followed by sequential addition of 0.2 mL 0.5% N-ethylmaleimide, 0.4 mL 10% TCA, 0.4 mL 44% phosphoric acid, 0.4 mL 4% α,α'-dipyridyl, and 0.2 mL 3% FeCl₃ (37°C, 30 min), with absorbance read at 525 nm; DHA content was determined similarly without DTT, and ASA calculated by difference. H₂O₂ content was measured by mixing 0.5 mL supernatant with 1 mL 0.1% TiCl₄ in 20% H₂S 2 O₄ (room temperature, 10 min), centrifuging (10,000×g, 10 min), and reading absorbance at 412 nm. GSH was determined by mixing 0.2 mL supernatant with 1.8 mL 0.1 M phosphate buffer (pH 8.0) and 0.1 mL 10 mM DTNB (room temperature, 5 min), measuring absorbance at 412 nm. Standard curves were generated using serial dilutions of each compound, with all measurements performed in triplicate and expressed as µmol/g FW. 2.4 Determination of antioxidant enzyme activity Fresh leaf tissue (0.5 g) was homogenized in 5 mL of ice-cold extraction buffer containing 50 mM phosphate buffer (pH 7.5), 1 mM EDTA, 1% (w/v) polyvinylpyrrolidone (PVP), and 0.1% (v/v) Triton X-100, followed by centrifugation at 15,000×g for 20 min at 4°C. The supernatant was used for enzyme assays with protein content determined by Bradford method using BSA as standard. All enzyme activities were determined using commercial assay kits (Solarbio Science & Technology Co., Ltd., Beijing, China) following the manufacturer's protocols. GalLDH (EC 1.1.1.117) activity was measured by monitoring NADH production at 340 nm in 1 mL reaction mixture containing 50 mM Tris-HCl (pH 7.5), 0.2 mM NAD⁺, and 5 mM L-galactono-1,4-lactone; AAO (EC 1.10.3.3) activity was determined by ASA oxidation at 265 nm in 50 mM phosphate buffer (pH 5.6) with 0.5 mM ASA; APX (EC 1.11.1.11) activity was assayed by monitoring ASA oxidation at 290 nm in 50 mM phosphate buffer (pH 7.0) containing 0.5 mM ASA and 0.1 mM H₂O₂; DHAR (EC 1.8.5.1) activity was measured by DHA reduction at 265 nm in 50 mM phosphate buffer (pH 7.0) with 2.5 mM GSH and 0.1 mM DHA; MDHAR (EC 1.6.5.4) activity was determined by NADH oxidation at 340 nm in 50 mM Tris-HCl (pH 7.5) containing 0.2 mM NADH and 2.5 mM MDHA; GR (EC 1.8.1.7) activity was assayed by NADPH oxidation at 340 nm in 50 mM phosphate buffer (pH 7.5) with 0.2 mM NADPH and 1 mM GSSG; SOD (EC 1.15.1.1) activity was determined by measuring inhibition of NBT photoreduction at 560 nm in 50 mM phosphate buffer (pH 7.8) containing 13 mM methionine, 75 µM NBT, 0.1 mM EDTA, and 2 µM riboflavin under 4000 lux illumination for 20 min (one unit defined as 50% inhibition); CAT (EC 1.11.1.6) activity was measured by H₂O₂ decomposition at 240 nm in 50 mM phosphate buffer (pH 7.0) with 15 mM H₂O₂. All assays were performed at 25°C with appropriate controls, and activities were expressed as nmol or µmol substrate converted min⁻¹ mg⁻¹ protein based on standard curves. Three biological replicates were performed for each measurement. 2.5 Measurement of the components of rice yield and the total biomass Following threshing, grain samples were processed using an electronic grain counter to separate and quantify mature grains. Yield components were evaluated by measuring: (1) the number of panicles per plant (NP), counted manually; (2) grains per panicle (GP), determined by averaging counts from 10 representative panicles; (3) grain-setting rate (GSR), calculated as (filled grains/total florets) × 100%; and (4) 1000-grain weight (GW), measured using an analytical balance (accuracy: 0.001 g) with three replicates. Theoretical yield per plant (TY, g/plant) was computed as: TY = NP×GP×GSR×GW×10⁻ 6 . For biomass determination, fresh weight (FW) was recorded immediately after harvest, and dry weight (DW) was obtained after oven-drying at 80°C for 96 h (until constant weight). Total biomass was expressed as both FW and DW, with moisture content calculated as [(FW − DW)/FW]×100%. Field yield is measured and converted into yield per hectare. 2.6 Measurement of rice quality index Grain quality was comprehensively evaluated through multiple parameters: Chalkiness degree (CD) and chalky grain rate (CGR) were measured using a rice appearance quality analyzer (JDMZ12, Dongfu Jiuheng Instrument Technology Co., Beijing, China) with three 100-grain replicates per treatment. Milling quality was assessed by calculating brown rice rate (BRR), milled rice rate (MRR), and head rice rate (HRR) (defined as grains ≥ 5/4 of full length) as weight percentages relative to rough rice using a standardized laboratory mill. Starch characteristics included amylose content (AC) determined with an amylose/amylopectin assay kit (K-AMYL, Megazyme, Ireland) and pasting properties (peak viscosity [PV], breakdown [BD], setback [SB], pasting temperature [PaT]) analyzed by Rapid Visco Analyzer (RVA 4800, Perten Instruments) following standard 12% flour concentration protocols. Protein content and composition were analyzed according to Faizan et al. ( 2021 ) and Fabian et al. (2011) methods respectively, using near-infrared spectroscopy (NIRS) with triplicate measurements. The content of 2-AP was determined by gas chromatography. Fresh rice samples (5.0 g) were ground in liquid nitrogen and extracted with 10 mL ultrapure water by vortexing for 1 min followed by ultrasonic extraction (40 kHz, 25°C) for 30 min. After centrifugation (10,000×g, 15 min, 4°C), the supernatant was filtered through 0.22 µm organic membrane filters for analysis. A series of 2-AP standard solutions (0, 0.1, 0.5, 1, 5, and 10 µg/mL) were prepared and processed identically. GC-MS analysis was performed using an Agilent 7890B-5977A system equipped with a DB-WAX capillary column (30 m×0.25 mm×0.25 µm) under the following conditions: injector temperature 250°C; ion source temperature 230°C; transfer line temperature 280°C; helium carrier gas at 1.0 mL/min; 1 µL injection in splitless mode; electron ionization (70 eV); and selected ion monitoring (m/z 83, 110, 111). The temperature program was: initial 50°C (hold 1 min), ramp at 10°C/min to 200°C, then 5°C/min to 240°C (hold 5 min). Quantification was performed using external standard calibration based on the characteristic ion (m/z 83) peak area, with LOD of 0.01 µg/kg and LOQ of 0.03 µg/kg. Method validation showed spiked recoveries of 85–112%, intra-day precision RSD < 5%, and inter-day precision RSD < 8%. 2.7 Determination of silicon content in rice The silicon content in rice was determined by inductively coupled plasma mass spectrometry (ICP-MS). Briefly, the finely powdered samples were digested with nitric acid using a closed-vessel microwave system, and the resulting solutions were analyzed by ICP-MS. Quantification was performed via external calibration using the ²⁹Si isotope monitored in helium collision mode to effectively eliminate polyatomic interferences. The results were calculated based on the measured concentration, blank correction, and sample mass, with all analyses conducted in triplicate to ensure reproducibility. 2.8 Statistical analysis All experimental procedures were conducted with ten independent biological replicates to ensure data reliability. For each measured parameter, the reported values represent the arithmetic mean ± standard deviation (SD) of these ten replicates. Statistical analyses were performed using SPSS software (version 26.0, IBM Corporation, Armonk, NY, USA). 3 Result 3.1 K₂Si₂O₅-mediated promotion of photosynthetic performance in rice As shown in Fig. 1 a, K 2 Si 2 O 5 significantly increased the net photosynthetic rate of rice leaves of the two hybrid rice varieties at each stage, and SI-3 treatment was the most significant increase in each stage. In addition, we also measured stomatal conductance ( Gs ), transpiration rate ( Tr ) and stomatal limitation ( Ls ) of rice leaves during the corresponding period. And the correlation analysis is carried out (Fig. 1 b and c). To investigate the alterations in photosynthetic mechanisms of rice leaves under silicon influence, we measured chlorophyll fluorescence parameters, antioxidant index RuBPcase activity, and malondialdehyde (MDA) content across different growth periods. This comprehensive approach allowed us to evaluate the effects of K 2 Si 2 O 5 on both the enzymatic and membrane systems involved in leaf electron transport (Figure S2). The results demonstrated that silicon application significantly enhanced photosynthetic performance in rice leaves. The net photosynthetic rate showed strong positive correlations with key photosynthetic indicators, including Fv/Fm (maximum quantum yield of PSII), Fv'/Fm' (effective quantum yield of PSII under light), qP (photochemical quenching coefficient), Y(II) (actual quantum yield of PSII), ETR (electron transport rate), and RuBPcase activity, as well as with the content of various photosynthetic pigments and total pigment concentration. Conversely, silicon application significantly reduced NPQ (non-photochemical quenching) and MDA accumulation (Figure These findings suggest that silicon improves leaf photosynthetic capacity by (1) enhancing quantum yield efficiency, (2) optimizing photochemical quenching, (3) promoting electron transport efficiency, and (4) boosting key photosynthetic enzyme activity, while simultaneously suppressing non-photochemical energy dissipation. Furthermore, silicon appears to strengthen both enzymatic and non-enzymatic antioxidant systems in leaves, reducing hydrogen peroxide accumulation. This protective effect helps maintain cellular membrane integrity and ensures normal metabolic processes, ultimately supporting enhanced photosynthetic performance. 3.2 The improvement of antioxidant capacity of rice leaves by silicon mainly depended on non-enzymatic antioxidant system Next, we independently studied the correlation between the antioxidative properties and H 2 O 2 of rice leaves under the regulation of silicon, in order to explore the key enzymes that play a role in antioxidant capacity. The results demonstrate that silicon (Si) regulation induces distinct antioxidant strategies in the two rice varieties, with both primarily utilizing the ascorbate-glutathione (ASA-GSH) cycle as their core non-enzymatic antioxidant system. However, notable varietal differences exist in their key enzymatic components. In XLY, the antioxidant response predominantly depends on galactono-1,4-lactone dehydrogenase (GalLDH), ascorbate oxidase (AAO), and glutathione reductase (GR). In contrast, QY employs a more comprehensive enzymatic network involving GalLDH, AAO, ascorbate peroxidase (APX), dehydroascorbate reductase (DHAR), and monodehydroascorbate reductase (MDHAR) (Fig. 2 b and c). These findings reveal that while silicon similarly activates non-enzymatic antioxidant systems in both varieties, it triggers fundamentally different enzymatic regulation mechanisms. 3.3 Silicon increases rice yield and biomass accumulation and redistributes yield components Finally, we measured the economic yield and biological yield of rice in the pot experiment stage, and tested the seeds to explore its yield composition (Fig. 3 a and Figure S3). The results showed that the Si-3 treatment showed the best results in both economic yield and biological yield, which was basically consistent with the net photosynthetic rate described above. However, yield and biomass are only one aspect of photosynthesis. For the pot experiment, quality measurement is not accurate, so we will repeat and verify the experiment in the field below to accurately explore the effect of silicon on rice quality and verify the conclusion that silicon improves rice yield To elucidate the relationship between photoassimilate allocation and yield formation, we systematically analyzed the correlation between net photosynthetic rate and yield components across different growth stages (Fig. 3 b). The results revealed distinct yield formation patterns between the two cultivars: for cultivar XLY, significant positive correlations were observed between photosynthetic activity and final biomass/yield at all growth stages, indicating that yield accumulation was achieved through balanced photoassimilate distribution throughout development. In contrast, cultivar QY exhibited a dynamic allocation pattern where the contribution of photoassimilate to yield gradually increased with plant development, while their contribution to biomass accumulation correspondingly decreased. Regarding yield component regulation, both cultivars showed similar trends: all yield components except grain filling rate (GSR) displayed relatively uniform accumulation patterns, while GSR progressively declined during grain development. These findings demonstrate that although the two cultivars differ in their photoassimilate partitioning strategies for biomass accumulation, they share common characteristics in yield component regulation, with GSR being the only key factor showing a consistent decreasing trend. 3.4 K 2 Si 2 O 5 also increased rice yield and improved rice quality in field condition In order to get close to the production and verify the previous results, we carried out experiments and measured the yield in the field cultivation environment (Fig. 4 a). In accordance with the results of pot experiment, Si-3 treatment was the most effective in increasing the yield of both rice varieties. This also means that the photosynthetic mechanism revealed in the pot experiment is relatively stable and reliable. In addition, we measured the quality of rice under field cultivation. In terms of processing quality, silicon significantly improved the yield of brown rice, milled rice and head rice rate, and the Si-3 treatment had the most significant improvement, and their values were increased by 8.6 (5.4) %, 7.8 (8.1) % and 8.4 (6.2) %, respectively, compared with the control group (Fig. 3 d). In addition, the content of rice protein and the changes of various protein components were also determined. As shown in the figure, silicon significantly increased the total protein content in rice, among which Si3 treatment had the most significant effect, and its value was increased by 9.9 (11.2) % compared with the control group. Compared with the control group, the contents of albumin, prolamin and gluten in Si treated XLY rice were significantly increased by 15.0%, 16.3% and 10.3%, respectively, while globulin was decreased by 17.2%. Compared with Control the protein content of Si-3 rice in QY increased by 11.5% (Albumin), 14.7% (Globulin), 7.2% (Prolamin) and 11.4% (Glutelin), respectively (Fig. 4 c). In addition, we also found that it can significantly improve the Chalkiness Degree and Chalky Grain Rate of rice, and also increase the 2-AP and AaC contents. At the same time, the AC content and AC/AaC were significantly reduced, and the changes were most significant after Si-3 treatment (Fig. 5 a). In addition, considering the relatively complex results of rice RVA (Fig. 5 b), with many positive and negative indicators, and the overall quality indicators, we conducted a comprehensive assignment weighted score for the quality indicators under each treatment based on principal component analysis, and the results were shown in Fig. 5 c. After comprehensive scoring, we found that the comprehensive quality of the two rice varieties showed a significant upward trend under the regulation of silicon, and the improvement of the two varieties was most significant under the treatment of Si-3. In order to further investigate the distribution of photocompound on rice quality, a structural equation model was established to investigate the effect of photocompound on rice quality under the control of silicon (Fig. 6 ). The results showed that the influence mechanism of photosynthate distribution under silicon control was basically the same for the two varieties. Photocontracides had positive effects on AaC content, 2-AP content and total protein content, but had negative effects on chalkness index and AC content. Comparing the two varieties, compared with QY, photocontractions focused on XLY in the aspects of Head Rice Rate, AaC Content, Chalkiness Degree and total protein, while other aspects focused more on QY. So far, we evaluated the photosynthetic capacity, antioxidant properties, yield and quality indexes of rice regulated by different amounts of silicon under artificial conditions and field conditions. The results showed that all indexes pointed to SI-3 treatment, that is, 0.75mM K 2 Si 2 O 5 treatment. 3.5The influence of exogenous silicon on the silicon content in rice The silicon content in rice samples under different silicon treatment concentrations was successfully determined using ICP-MS, revealing a clear dose-dependent increase in silicon accumulation as the exogenous silicon application level rose from 0 to 1.25 mg/kg (Fig. 4 b). The control group without silicon addition showed a low background silicon level, while at 0.25 mg/kg, the silicon content in the two biological replicates ( XLY and QY ) increased to 16.88 mg/kg and 15.32 mg/kg, respectively. As the treatment concentration reached 1.25 mg/kg, silicon accumulation peaked at 42.24 mg/kg for XLY and 40.08 mg/kg for QY , both significantly higher than the control. These results demonstrate a strong silicon enrichment capacity in rice and a notable positive correlation between silicon content and treatment concentration, confirming that exogenous silicon application can effectively modulate silicon levels in rice. 4 Discussion Rice, serving as a principal staple crop globally, plays a fundamental role in maintaining food security and supporting sustainable agricultural systems (Wu et al., 2025 ). Silicon (Si), widely acknowledged as a beneficial quasi-essential element for plant development, has shown remarkable capacity to improve photosynthetic performance, enhance mechanical strength (thereby reducing lodging risk), and boost stress tolerance across diverse crop species (Gulzar et al., 2025 ). Despite these recognized benefits, there remains a paucity of comprehensive studies examining the integrated effects of silicon on photosynthetic characteristics, antioxidant defense systems, yield potential, and grain quality parameters in rice. Understanding the mechanistic basis of silicon-mediated improvements in rice production carries substantial implications for addressing global food security challenges and advancing sustainable farming practices. In the present investigation, we implemented a foliar application approach using graded concentrations of potassium silicate (K 2 Si 2 O 5 ) to systematically evaluate its physiological and agronomic impacts. Through meticulous measurement of photosynthetic indices and antioxidant profiles at critical growth stages, coupled with thorough assessments of field performance and grain quality attributes, this study provides crucial insights for developing optimized silicon management protocols in rice cultivation, with the ultimate goal of simultaneously enhancing both productivity and grain quality. 4.1 Silicon can mobilize various photosynthetic mechanisms in rice leaves to promote photosynthesis collaboratively Previous studies have well documented the positive effects of silicon on plant photosynthesis across various species. Zhang et al. ( 2018 ) demonstrated that silicon application significantly enhances the net photosynthetic rate and stress resistance in tomato leaves. Similar findings were reported in maize (Xie et al., 2015 ), cucumber (Hattori et al., 2008 ), and banana (Asmar et al., 2013 ). However, such systematic investigations remain scarce in rice plants. Our current study not only confirms the photosynthetic-promoting effect of silicon in rice, consistent with previous findings in other crops, but also reveals stage-specific regulatory mechanisms: during vegetative growth, silicon primarily enhances photosynthesis by modulating stomatal behavior, while post-flowering stages involve more complex photosynthetic pathway regulation. Although silicon is not a structural component of photosynthetic pigments, it may indirectly increase pigment content by facilitating magnesium uptake (dos Santos et al., 2010 ), which likely explains the elevated chlorophyll and carotenoid levels observed in our study. Additionally, silicon contributes to photosynthetic improvement through multiple mechanisms: (1) modifying cell wall architecture to improve stem erectness and light interception (Pan et al., 2024); (2) mediating stomatal regulation via hormonal pathways (Park et al., 2024 ), as evidenced in our experimental results. These findings suggest that the enhanced photosynthetic capacity in rice leaves results from the synergistic effects of these multifaceted regulatory mechanisms. 4.2 Silicon can mobilize the antioxidant system of rice plants to resist adverse external environments Antioxidant capacity plays a crucial role in plant growth and development. Previous studies have demonstrated that silicon can significantly enhance the antioxidant potential in plants, as shown by Xu et al. ( 2023 ) in strawberry plants where silicon improved both yield and quality. Our findings reveal that silicon similarly activates both enzymatic and non-enzymatic antioxidant systems in rice plants. The antioxidant effects of silicon operate through multiple interconnected mechanisms. First, silicon deposits on cell walls to form a protective silicon-cellulose composite layer that enhances mechanical strength while reducing pathogen invasion and mechanical damage, thereby minimizing ROS generation under various stresses (Sanadhya et al., 2025 ). Additionally, silicon promotes the biosynthesis of key antioxidants including glutathione (GSH), ascorbic acid (AsA), and phenolic compounds that directly neutralize ROS. Another important mechanism involves silicon's ability to chelate transition metals like Fe 2+ and Cu + , thereby inhibiting the ROS-generating Fenton reaction (Yang et al., 2024 ). Furthermore, silicon contributes to antioxidant defense through heavy metal chelation and modulation of hormonal regulation systems (Rachappanavar et al., 2024 ; Wang et al., 2024 ). Importantly, the enhancement of the antioxidant system by silicon provides critical protection for photosynthetic apparatus, particularly under stress conditions. This protective effect represents a fundamental mechanism underlying the observed improvements in photosynthetic capacity, as the reinforced antioxidant system prevents oxidative damage to chloroplasts and maintains optimal photosynthetic function. 4.3 Silicon could have a positive effect on rice yield Numerous studies have demonstrated the yield-enhancing effects of silicon application in various crops. As evidenced by Dou et al. ( 2023 ) in tomatoes and Artyszak et al. ( 2015 ) in sugar beets, silicon supplementation consistently improves crop productivity. Our findings corroborate these observations, showing that silicon application significantly increases both grain yield and biomass accumulation in rice under both field conditions and controlled phenological cultivation. The yield improvement can be attributed to multiple physiological mechanisms: (1) enhanced root system development, where silicon promotes root formation and increases surface area, thereby improving the uptake of essential macronutrients including nitrogen, phosphorus, and potassium (Saja-Garbarz et al., 2024 ); (2) optimized plant architecture that facilitates better light interception and photosynthetic efficiency at the canopy level (Thakral et al., 2024 ); and (3) improved photosynthetic performance and antioxidant capacity as discussed previously. These synergistic effects collectively contribute to the observed increases in both economic yield (grain production) and biological yield (total biomass) in rice. 4.4 Silicon can comprehensively enhance the quality of rice Current understanding of silicon's role in regulating rice grain quality remains limited, largely due to the complexity of quality evaluation parameters and incomplete elucidation of the underlying physiological mechanisms. This study reveals that silicon application significantly enhances both the content and proportion of translucent endosperm in rice grains. These improvements may be attributed to silicon's ability to upregulate key enzymatic activities in the sucrose-to-starch conversion pathway, particularly during the critical grain-filling stage (Peng et al., 2025 ). The silicon-induced modifications in starch composition confer multiple quality enhancements: by reducing chalkiness formation and improving grain transparency, silicon elevates the appearance quality of rice. Simultaneously, it optimizes starch deposition patterns within the endosperm, thereby increasing grain structural integrity and subsequently reducing breakage rates during milling (Yan et al., 2024 ; Zhang et al., 2024). These findings provide novel insights into silicon fertilization as a viable strategy for concurrently improving both milling yield and visual quality characteristics in rice production systems. Emerging evidence highlights silicon's multifaceted role in enhancing rice grain quality through physiological and biochemical regulation. Notably, silicon facilitates protein accumulation in grains by improving nitrogen uptake and translocation, while simultaneously modulating the gluten-to-prolamin ratio and increasing essential amino acid content, particularly lysine, thereby elevating the nutritional profile of rice. This synergistic effect proves especially pronounced under nitrogen-limited conditions (Yamaji et al., 2024 ). Concurrently, silicon sustains photosynthetic capacity during grain filling by boosting the activity of key antioxidant enzymes (SOD, POD) in flag leaves, effectively delaying leaf senescence and promoting more complete, uniform grain development - fundamental prerequisites for superior milling and appearance quality (Mushtaq et al., 2024 ). Perhaps most remarkably, silicon influences secondary metabolic pathways to enhance biosynthesis of characteristic aroma compounds, including 2-acetyl-1-pyrroline, ultimately improving the aromatic qualities of rice (Praseartkul et al., 2022 ). 5 Conclusion In conclusion, this study demonstrates that potassium silicate (K 2 Si 2 O 5 ) application significantly enhances rice productivity and quality through multiple physiological mechanisms. The key findings can be summarized as follows: First, K 2 Si 2 O 5 improves photosynthetic performance across all growth stages by regulating both stomatal conductance and electron transport systems, as evidenced by enhanced Fv/Fm, qP, ETR values, photosynthetic pigment content, and key enzyme activities. Second, it strengthens the plant's antioxidant defense system through coordinated activation of both enzymatic (CAT, SOD) and non-enzymatic (ASA-GSH cycle) components. Third, K 2 Si 2 O 5 treatment optimizes yield components, leading to significant increases in both grain yield and total biomass accumulation. Notably, K 2 Si 2 O 5 application comprehensively improves multiple grain quality parameters, including processing characteristics, appearance, palatability, nutritional value (through increased essential amino acids), and aromatic compound content. Among the tested concentrations, the 0.75 mM pure silicon treatment (Si-3) showed the most pronounced effects across all measured parameters, suggesting its optimal potential for practical application in rice production systems to simultaneously enhance both yield and quality. These findings provide strong evidence for incorporating silicon fertilization as an effective agronomic practice in rice cultivation systems aiming for improved productivity and premium grain quality. 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19:55:13","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153467,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25035040structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/81e54679d6cbaf083340eea9.xml"},{"id":92444680,"identity":"c4ce5641-35b4-49ef-95c2-2512aed5fc0f","added_by":"auto","created_at":"2025-09-29 19:55:13","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165814,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/3cc638bc307c0116f45878d5.html"},{"id":92446292,"identity":"de607700-7013-482d-a541-2f3c433c065c","added_by":"auto","created_at":"2025-09-29 20:19:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80946,"visible":true,"origin":"","legend":"\u003cp\u003eRevealing the dynamic changes and physiological mechanisms of rice peaf photosynthesis under different silicon addition levels. (\u003cstrong\u003ea\u003c/strong\u003e) The dynamic changes of photosynthetic rate of rice leaves during different growth stages. (\u003cstrong\u003eb\u003c/strong\u003e) The dynamic changes in photosynthetic physiological parameters of rice leaves in different periods. (\u003cstrong\u003ec\u003c/strong\u003e) Correlation analysis of photosynthetic physiological parameters of rice leaves during different growth stages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Value represent mean±standard deviation (SD) of ten replicates for each treatment. The different small letters indicate significant difference at \u003cem\u003eP≤0.05\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eXLY\u003c/strong\u003e\u003c/em\u003e: Xinliangyou-223, \u003cem\u003e\u003cstrong\u003eQY\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Quanyouefengsimiao.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/2723e9428d4d0e410c7e9cfd.jpg"},{"id":92445523,"identity":"f324fd76-75f9-47e8-a55d-2cee5327ee63","added_by":"auto","created_at":"2025-09-29 20:03:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120000,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of antioxidant system changes in rice leaves under silicon regulation. (\u003cstrong\u003ea\u003c/strong\u003e) Correlation analysis of photosynthetic characteristics and antioxidant characteristics of rice leaves. (\u003cstrong\u003eb\u003c/strong\u003e) Screening of main antioxidant factors in rice at different growth stages. (\u003cstrong\u003ec\u003c/strong\u003e) The changing trends of key antioxidant factors in two rice varieties at different growth stages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Value represent mean±standard deviation (SD) of ten replicates for each treatment. The different small letters indicate significant difference at \u003cem\u003eP≤0.05\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eXLY\u003c/strong\u003e\u003c/em\u003e: Xinliangyou-223, \u003cem\u003e\u003cstrong\u003eQY\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Quanyouefengsimiao.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/dab5317822f3709b589a1157.jpg"},{"id":92444648,"identity":"213fc315-1e53-44a3-aad8-17f584d4962a","added_by":"auto","created_at":"2025-09-29 19:55:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88101,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of different silicon treatments on rice yield, appearance and processing characteristics. (\u003cstrong\u003ea\u003c/strong\u003e) Changes in single plant yield and biomass. (\u003cstrong\u003eb\u003c/strong\u003e) Correlation analysis between yield components and photosynthetic efficiency. (\u003cstrong\u003ec\u003c/strong\u003e) Changes in the appearance quality of rice under different silicon treatments. (\u003cstrong\u003ed\u003c/strong\u003e) Changes in rice processing quality under different silicon treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Value represent mean±standard deviation (SD) of ten replicates for each treatment. The different small letters indicate significant difference at \u003cem\u003eP≤0.05\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eXLY\u003c/strong\u003e\u003c/em\u003e: Xinliangyou-223, \u003cem\u003e\u003cstrong\u003eQY\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Quanyouefengsimiao.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/52ef29c0f4302adba1e481be.jpg"},{"id":92445527,"identity":"9f9896ca-1c47-4b3d-976b-2ad5369b1fdd","added_by":"auto","created_at":"2025-09-29 20:03:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86650,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in rice field yield, grain silicon content and protein quality under different silicon concentration treatments. (\u003cstrong\u003ea\u003c/strong\u003e) Trend of rice yield in the field. (\u003cstrong\u003eb\u003c/strong\u003e) The silicon content in rice. (\u003cstrong\u003ec\u003c/strong\u003e) The trend of protein components in rice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Value represent mean±standard deviation (SD) of ten replicates for each treatment. The different small letters indicate significant difference at \u003cem\u003eP≤0.05\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eXLY\u003c/strong\u003e\u003c/em\u003e: Xinliangyou-223, \u003cem\u003e\u003cstrong\u003eQY\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Quanyouefengsimiao.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/63aac04abe2b7dc7ec826be2.jpg"},{"id":92444649,"identity":"9d210369-5b86-4538-9b72-ba509300d92a","added_by":"auto","created_at":"2025-09-29 19:55:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":73611,"visible":true,"origin":"","legend":"\u003cp\u003eRice sensory quality and overall evaluation.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Changes in rice aroma substances and starch ratio. (\u003cstrong\u003eb\u003c/strong\u003e) The influence of silicon on the RVA spectrum of rice. (\u003cstrong\u003ec\u003c/strong\u003e) Weighted replication scoring of rice quality based on principal component analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Value represent mean±standard deviation (SD) of ten replicates for each treatment. The different small letters indicate significant difference at \u003cem\u003eP≤0.05\u003c/em\u003e. \u003cem\u003e\u003cstrong\u003eXLY\u003c/strong\u003e\u003c/em\u003e: Xinliangyou-223, \u003cem\u003e\u003cstrong\u003eQY\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Quanyouefengsimiao.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/453282c06890acc04971ae00.jpg"},{"id":92444651,"identity":"4196ec05-18da-4373-b06d-534b5eff55e1","added_by":"auto","created_at":"2025-09-29 19:55:12","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48832,"visible":true,"origin":"","legend":"\u003cp\u003eThrough the construction of structural equations, the physiological effects of silicon on two rice varieties were revealed.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/f01956f7c5c20cff3b1214a9.jpg"},{"id":94826355,"identity":"e4867372-4a0c-4394-ab5c-2d1ad6ce8b40","added_by":"auto","created_at":"2025-10-31 06:51:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1576784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/75f2e978-cab6-4213-8efe-f1fe2a94a787.pdf"},{"id":92444657,"identity":"ffd80c4d-210e-4d4f-ad1d-ff1152ff78c8","added_by":"auto","created_at":"2025-09-29 19:55:12","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":890966,"visible":true,"origin":"","legend":"","description":"","filename":"supplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-7606764/v1/136f6e94dc4ab390a2d0c1d9.docx"}],"financialInterests":"","formattedTitle":"From Photosynthesis to Antioxidants: How Silicon (K₂Si₂O₅) Improves Yield and Grain Quality in Rice","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa L.\u003c/em\u003e) serves as a critical global food staple, constituting the primary dietary energy source for over 25% of the world\u0026rsquo;s population. While current production levels satisfy global caloric demand, the agricultural research paradigm is undergoing a strategic shift\u0026mdash;from yield maximization to quality-driven cultivation (Mathan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This transition reflects evolving consumer demands for superior nutritional profiles, sensory attributes, and functional properties in rice (Hori et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs the most essential biochemical process on Earth, photosynthesis serves as the primary energy conversion mechanism that sustains virtually all terrestrial life, with particular significance in agricultural systems, where it fundamentally determines both crop yield and quality (Eckardt et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Extensive research has established a robust physiological linkage between leaf photosynthetic performance and seed quality parameters, demonstrating that enhanced photosynthetic capacity serves as a pivotal strategy for simultaneously improving productivity and nutritional value (Shao et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePioneering studies by Sun et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed that optimizing photosynthesis in millet (\u003cem\u003ePanicum miliaceum L\u003c/em\u003e) significantly increased grain soluble solids (by 23%) and total phenolic content (by 18%), while Zhao et al., (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that heightened photosynthetic activity in sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e) enhanced both biomass accumulation and key quality traits. These findings have been consistently validated across major cereal crops, including wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e), where improved photosynthesis correlates with higher protein content and baking quality (Xu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e) (Gao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which exhibits increased β-glucan levels; and maize (\u003cem\u003eZea mays\u003c/em\u003e), where photosynthetic optimization boosts both starch content and carotenoid concentrations (Teng et al., 2025). This photosynthesis-quality nexus operates through multiple pathways, including carbon allocation dynamics that enhance grain filling, increased synthesis of secondary metabolites such as phenolics and flavonoids, and improved stress mitigation through enhanced antioxidant capacity (Tong et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The collective evidence underscores that strategic enhancement of photosynthetic efficiency\u0026mdash;whether through breeding, agronomic practices, or biotechnological approaches\u0026mdash;represents a transformative solution for addressing both global productivity and nutritional security challenges in sustainable agriculture.\u003c/p\u003e\u003cp\u003eSilicon (Si) has emerged as a functionally significant beneficial element for higher plants, with particularly pronounced growth-enhancing and stress-mitigating properties observed in monocotyledonous species such as rice, sugarcane (\u003cem\u003eSaccharum officinarum\u003c/em\u003e) (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and bamboo (\u003cem\u003eBambusoideae\u003c/em\u003e) (Guo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While not currently recognized as an essential micronutrient, extensive phytophysiological research has unequivocally established its critical role in plant-environment interactions within agricultural ecosystems.\u003c/p\u003e\u003cp\u003eCutting-edge studies utilizing advanced microscopy techniques have revealed that silicon undergoes specific biomineralization processes in plant tissues, forming specialized phytolithic structures within epidermal cell walls (Zhou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This unique biosilicification mechanism confers remarkable biomechanical advantages, including enhanced culm strength (increasing stem rigidity by 30\u0026ndash;50%), improved resistance to lodging, and optimized canopy architecture through promoted vertical growth orientation (Mullangie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Beyond its structural functions, silicon exhibits sophisticated dual-phase defense modulation: it establishes physical barriers through silica deposition while simultaneously priming systemic resistance pathways, thereby providing comprehensive protection against diverse biotic stressors (Zhu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOf particular agricultural relevance is silicon's multifaceted role in photosynthetic optimization and abiotic stress amelioration. Current research demonstrates that silicon supplementation can enhance photosynthetic quantum yield by 15\u0026ndash;20% through improved light interception efficiency (Xu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, it orchestrates a sophisticated antioxidant network, upregulating key enzymes including superoxide dismutase (SOD) and catalase (CAT), which collectively enhance plant resilience to environmental challenges (Pang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These scientifically validated benefits position silicon as an invaluable component in sustainable crop management systems and precision agriculture applications. Hasan et al. (2024) showed that nano-sized silicon can overcome soil acidification, adjust continuous cropping obstacles, improve photosynthetic efficiency of capsella bursa seedlings, and enhance the function of non-enzymatic antioxidant system of plants. Nowakowska et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have also shown that silicon can improve the photosynthetic efficiency of European beech trees under drought conditions, thereby improving their growth. Similar studies have been done on cherry tomatoes (\u003cem\u003eLycopersicon esculentum var. cerasiforme A.Gray\u003c/em\u003e) (Kobayakawa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), kale (\u003cem\u003eBrassica oleracea var. acephala DC.\u003c/em\u003e) (Tong et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and citrus (\u003cem\u003eCitrus reticulata Blanco\u003c/em\u003e) (jhazmat et al., 2024).\u003c/p\u003e\u003cp\u003eHowever, up to now, there have been few reports on the mechanism of regulating photosynthesis and antioxidant capacity of rice, thus regulating rice quality and yield. It is important to study the effect of silicon on rice yield and quality for the optimization of rice production. Therefore, in this study, rice was treated with different concentrations of K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e, and photosynthetic related indexes of rice were measured at each stage to reveal the pre-related mechanism of silicon regulation of rice photosynthesis, explore its antioxidant properties at the same time, and finally evaluate the quality of rice, in order to fully reveal the silicon-mediated photosynthesis and antioxidant system of rice. Then the physiological mechanism of rice quality was regulated, and the theoretical basis was provided for increasing rice yield and improving rice quality and ensuring food security.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Plant material, growth conditions, and exogenous treatments\u003c/h2\u003e\u003cp\u003eThis study employed two commercially important hybrid rice cultivars: Quanyouefengsimiao (QY) and Xinliangyou-223 (XLY), both with a 135-day growth cycle. The pot experiment was conducted at Yangtze University's experimental station in Jingzhou, China (30\u0026deg;32'6\"N, 112\u0026deg;12'3\"E) during the 2022\u0026ndash;2024 growing season (June-November), all the data in this experiment were analyzed based on the average values obtained from the three-year experiment.. Following standard protocols, seeds were surface-sterilized and soaked in distilled water at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1℃ for 24 h, then germinated at 30℃ in darkness. Uniformly germinated seeds were selected and transplanted into plastic nursery pots containing characterized growth substrate (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). After 25 days of seedling establishment, plants were transplanted into 38\u0026times;38\u0026times;41 cm (L\u0026times;W\u0026times;H) cultivation pots, each containing 8 kg of growth medium amended with 8 g of compound fertilizer (N\u0026thinsp;\u0026ge;\u0026thinsp;16%, P\u0026thinsp;\u0026ge;\u0026thinsp;9%, K\u0026thinsp;\u0026ge;\u0026thinsp;15%). The experimental setup consisted of three planting holes per pot with two seedlings per hole. All pots were maintained in a controlled-environment greenhouse with diurnal temperature regulation (25℃ night/30℃ day), 60% relative humidity, and natural photoperiod. Soil moisture was maintained at field capacity through bi-daily irrigation with distilled water.\u003c/p\u003e\u003cp\u003eThis study implemented five selenium (Si) treatment levels (0 [Control], 0.25 [Si-1], 0.5 [Si-2], 0.75 [Si-3], 1 [Si-4] and 1.25 [Si-5] mM pure silicon) applied as K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e through foliar spraying during three critical growth phases: early tillering, flowering, and one week post-flowering. Photosynthetic parameters were systematically measured at six developmental stages (tillering, jointing, heading, milk ripening, wax ripening, and full maturity), while yield components (panicle number, grain weight, filled grain percentage). The experiment followed a completely randomized design with three biological replicates per treatment, totaling 30 experimental units, with all measurements conducted using standardized protocols and properly calibrated instruments.\u003c/p\u003e\u003cp\u003eField validation was conducted under standard field cultivation conditions, with the experimental layout illustrated in the accompanying \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. Seedling cultivation followed identical protocols to the pot experiments. The experimental design comprised 36 plots arranged in three complete replications. At maturity stage, comprehensive assessments were performed including both yield and grain quality parameters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Determination of photosynthetic parameters of plant leaves\u003c/h2\u003e\u003cp\u003ePhotosynthetic physiological parameters were measured using a Li-6400 portable photosynthesis system (LI-COR Inc., Lincoln, NE, USA) on clear mornings between 9:00 and 11:00 AM, with measurements performed on fully expanded trifoliate leaves during tillering and jointing stages and flag leaves during subsequent growth stages to determine net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and stomatal limitation value (Ls). Chlorophyll fluorescence parameters, including actual quantum yield of PSII [Y(II)], maximum quantum yield of PSII (Fv/Fm), effective quantum yield of PSII (Fv'/Fm'), electron transport rate (ETR), non-photochemical quenching (NPQ), and photochemical quenching coefficient (qP), were analyzed using a PAM-2500 portable pulse-modulated fluorometer (Heinz Walz GmbH, Effeltrich, Germany) after 2 h of dark adaptation. Additionally, photosynthetic pigment concentrations (Chla, Chlb, and Car) were determined spectrophotometrically according to Zhu et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) by measuring absorbance at 665, 649, and 470 nm, while Rubisco (EC 4.1.1.39) activity was assayed using a commercial kit (Solarbio Science \u0026amp; Technology, Beijing, China), with enzyme activity defined as the amount required to oxidize 1 nmol NADH per minute per gram fresh weight at 25℃ (A340), and malondialdehyde (MDA) content was quantified following Zhu et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with spectrophotometric measurements at 450, 532, and 600 nm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Determination of antioxidant content\u003c/h2\u003e\u003cp\u003eThe contents of ascorbic acid (ASA), dehydroascorbic acid (DHA), hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e), and reduced glutathione (GSH) were determined spectrophotometrically using the following procedures: Fresh leaf tissue (0.5 g) was homogenized in 5 mL ice-cold 50 mM phosphate buffer (pH 7.8) and centrifuged (12,000\u0026times;g, 15 min, 4\u0026deg;C), with the supernatant collected for analysis. For ASA/DHA determination, 0.5 mL supernatant was mixed with 0.5 mL 10% TCA and divided: total ascorbate (ASA\u0026thinsp;+\u0026thinsp;DHA) was measured by adding 0.2 mL 0.4 M phosphate buffer (pH 7.4) and 0.2 mL 10 mM DTT (37\u0026deg;C, 15 min), followed by sequential addition of 0.2 mL 0.5% N-ethylmaleimide, 0.4 mL 10% TCA, 0.4 mL 44% phosphoric acid, 0.4 mL 4% α,α'-dipyridyl, and 0.2 mL 3% FeCl₃ (37\u0026deg;C, 30 min), with absorbance read at 525 nm; DHA content was determined similarly without DTT, and ASA calculated by difference. H₂O₂ content was measured by mixing 0.5 mL supernatant with 1 mL 0.1% TiCl₄ in 20% H₂S\u003csub\u003e2\u003c/sub\u003eO₄ (room temperature, 10 min), centrifuging (10,000\u0026times;g, 10 min), and reading absorbance at 412 nm. GSH was determined by mixing 0.2 mL supernatant with 1.8 mL 0.1 M phosphate buffer (pH 8.0) and 0.1 mL 10 mM DTNB (room temperature, 5 min), measuring absorbance at 412 nm. Standard curves were generated using serial dilutions of each compound, with all measurements performed in triplicate and expressed as \u0026micro;mol/g FW.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Determination of antioxidant enzyme activity\u003c/h2\u003e\u003cp\u003eFresh leaf tissue (0.5 g) was homogenized in 5 mL of ice-cold extraction buffer containing 50 mM phosphate buffer (pH 7.5), 1 mM EDTA, 1% (w/v) polyvinylpyrrolidone (PVP), and 0.1% (v/v) Triton X-100, followed by centrifugation at 15,000\u0026times;g for 20 min at 4\u0026deg;C. The supernatant was used for enzyme assays with protein content determined by Bradford method using BSA as standard. All enzyme activities were determined using commercial assay kits (Solarbio Science \u0026amp; Technology Co., Ltd., Beijing, China) following the manufacturer's protocols. GalLDH (EC 1.1.1.117) activity was measured by monitoring NADH production at 340 nm in 1 mL reaction mixture containing 50 mM Tris-HCl (pH 7.5), 0.2 mM NAD⁺, and 5 mM L-galactono-1,4-lactone; AAO (EC 1.10.3.3) activity was determined by ASA oxidation at 265 nm in 50 mM phosphate buffer (pH 5.6) with 0.5 mM ASA; APX (EC 1.11.1.11) activity was assayed by monitoring ASA oxidation at 290 nm in 50 mM phosphate buffer (pH 7.0) containing 0.5 mM ASA and 0.1 mM H₂O₂; DHAR (EC 1.8.5.1) activity was measured by DHA reduction at 265 nm in 50 mM phosphate buffer (pH 7.0) with 2.5 mM GSH and 0.1 mM DHA; MDHAR (EC 1.6.5.4) activity was determined by NADH oxidation at 340 nm in 50 mM Tris-HCl (pH 7.5) containing 0.2 mM NADH and 2.5 mM MDHA; GR (EC 1.8.1.7) activity was assayed by NADPH oxidation at 340 nm in 50 mM phosphate buffer (pH 7.5) with 0.2 mM NADPH and 1 mM GSSG; SOD (EC 1.15.1.1) activity was determined by measuring inhibition of NBT photoreduction at 560 nm in 50 mM phosphate buffer (pH 7.8) containing 13 mM methionine, 75 \u0026micro;M NBT, 0.1 mM EDTA, and 2 \u0026micro;M riboflavin under 4000 lux illumination for 20 min (one unit defined as 50% inhibition); CAT (EC 1.11.1.6) activity was measured by H₂O₂ decomposition at 240 nm in 50 mM phosphate buffer (pH 7.0) with 15 mM H₂O₂. All assays were performed at 25\u0026deg;C with appropriate controls, and activities were expressed as nmol or \u0026micro;mol substrate converted min⁻\u0026sup1; mg⁻\u0026sup1; protein based on standard curves. Three biological replicates were performed for each measurement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Measurement of the components of rice yield and the total biomass\u003c/h2\u003e\u003cp\u003eFollowing threshing, grain samples were processed using an electronic grain counter to separate and quantify mature grains. Yield components were evaluated by measuring: (1) the number of panicles per plant (NP), counted manually; (2) grains per panicle (GP), determined by averaging counts from 10 representative panicles; (3) grain-setting rate (GSR), calculated as (filled grains/total florets) \u0026times; 100%; and (4) 1000-grain weight (GW), measured using an analytical balance (accuracy: 0.001 g) with three replicates. Theoretical yield per plant (TY, g/plant) was computed as: TY\u0026thinsp;=\u0026thinsp;NP\u0026times;GP\u0026times;GSR\u0026times;GW\u0026times;10⁻\u003csup\u003e6\u003c/sup\u003e. For biomass determination, fresh weight (FW) was recorded immediately after harvest, and dry weight (DW) was obtained after oven-drying at 80\u0026deg;C for 96 h (until constant weight). Total biomass was expressed as both FW and DW, with moisture content calculated as [(FW\u0026thinsp;\u0026minus;\u0026thinsp;DW)/FW]\u0026times;100%. Field yield is measured and converted into yield per hectare.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Measurement of rice quality index\u003c/h2\u003e\u003cp\u003eGrain quality was comprehensively evaluated through multiple parameters: Chalkiness degree (CD) and chalky grain rate (CGR) were measured using a rice appearance quality analyzer (JDMZ12, Dongfu Jiuheng Instrument Technology Co., Beijing, China) with three 100-grain replicates per treatment. Milling quality was assessed by calculating brown rice rate (BRR), milled rice rate (MRR), and head rice rate (HRR) (defined as grains\u0026thinsp;\u0026ge;\u0026thinsp;5/4 of full length) as weight percentages relative to rough rice using a standardized laboratory mill. Starch characteristics included amylose content (AC) determined with an amylose/amylopectin assay kit (K-AMYL, Megazyme, Ireland) and pasting properties (peak viscosity [PV], breakdown [BD], setback [SB], pasting temperature [PaT]) analyzed by Rapid Visco Analyzer (RVA 4800, Perten Instruments) following standard 12% flour concentration protocols. Protein content and composition were analyzed according to Faizan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Fabian et al. (2011) methods respectively, using near-infrared spectroscopy (NIRS) with triplicate measurements.\u003c/p\u003e\u003cp\u003eThe content of 2-AP was determined by gas chromatography. Fresh rice samples (5.0 g) were ground in liquid nitrogen and extracted with 10 mL ultrapure water by vortexing for 1 min followed by ultrasonic extraction (40 kHz, 25\u0026deg;C) for 30 min. After centrifugation (10,000\u0026times;g, 15 min, 4\u0026deg;C), the supernatant was filtered through 0.22 \u0026micro;m organic membrane filters for analysis. A series of 2-AP standard solutions (0, 0.1, 0.5, 1, 5, and 10 \u0026micro;g/mL) were prepared and processed identically. GC-MS analysis was performed using an Agilent 7890B-5977A system equipped with a DB-WAX capillary column (30 m\u0026times;0.25 mm\u0026times;0.25 \u0026micro;m) under the following conditions: injector temperature 250\u0026deg;C; ion source temperature 230\u0026deg;C; transfer line temperature 280\u0026deg;C; helium carrier gas at 1.0 mL/min; 1 \u0026micro;L injection in splitless mode; electron ionization (70 eV); and selected ion monitoring (m/z 83, 110, 111). The temperature program was: initial 50\u0026deg;C (hold 1 min), ramp at 10\u0026deg;C/min to 200\u0026deg;C, then 5\u0026deg;C/min to 240\u0026deg;C (hold 5 min). Quantification was performed using external standard calibration based on the characteristic ion (m/z 83) peak area, with LOD of 0.01 \u0026micro;g/kg and LOQ of 0.03 \u0026micro;g/kg. Method validation showed spiked recoveries of 85\u0026ndash;112%, intra-day precision RSD\u0026thinsp;\u0026lt;\u0026thinsp;5%, and inter-day precision RSD\u0026thinsp;\u0026lt;\u0026thinsp;8%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Determination of silicon content in rice\u003c/h2\u003e\u003cp\u003eThe silicon content in rice was determined by inductively coupled plasma mass spectrometry (ICP-MS). Briefly, the finely powdered samples were digested with nitric acid using a closed-vessel microwave system, and the resulting solutions were analyzed by ICP-MS. Quantification was performed via external calibration using the \u0026sup2;⁹Si isotope monitored in helium collision mode to effectively eliminate polyatomic interferences. The results were calculated based on the measured concentration, blank correction, and sample mass, with all analyses conducted in triplicate to ensure reproducibility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll experimental procedures were conducted with ten independent biological replicates to ensure data reliability. For each measured parameter, the reported values represent the arithmetic mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) of these ten replicates. Statistical analyses were performed using SPSS software (version 26.0, IBM Corporation, Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 K₂Si₂O₅-mediated promotion of photosynthetic performance in rice\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e significantly increased the net photosynthetic rate of rice leaves of the two hybrid rice varieties at each stage, and SI-3 treatment was the most significant increase in each stage. In addition, we also measured stomatal conductance (\u003cem\u003eGs\u003c/em\u003e), transpiration rate (\u003cem\u003eTr\u003c/em\u003e) and stomatal limitation (\u003cem\u003eLs\u003c/em\u003e) of rice leaves during the corresponding period. And the correlation analysis is carried out (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb and c). To investigate the alterations in photosynthetic mechanisms of rice leaves under silicon influence, we measured chlorophyll fluorescence parameters, antioxidant index RuBPcase activity, and malondialdehyde (MDA) content across different growth periods. This comprehensive approach allowed us to evaluate the effects of K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e on both the enzymatic and membrane systems involved in leaf electron transport (Figure S2). The results demonstrated that silicon application significantly enhanced photosynthetic performance in rice leaves. The net photosynthetic rate showed strong positive correlations with key photosynthetic indicators, including Fv/Fm (maximum quantum yield of PSII), Fv\u0026apos;/Fm\u0026apos; (effective quantum yield of PSII under light), qP (photochemical quenching coefficient), Y(II) (actual quantum yield of PSII), ETR (electron transport rate), and RuBPcase activity, as well as with the content of various photosynthetic pigments and total pigment concentration. Conversely, silicon application significantly reduced NPQ (non-photochemical quenching) and MDA accumulation (Figure\u003c/p\u003e\n \u003cp\u003eThese findings suggest that silicon improves leaf photosynthetic capacity by (1) enhancing quantum yield efficiency, (2) optimizing photochemical quenching, (3) promoting electron transport efficiency, and (4) boosting key photosynthetic enzyme activity, while simultaneously suppressing non-photochemical energy dissipation. Furthermore, silicon appears to strengthen both enzymatic and non-enzymatic antioxidant systems in leaves, reducing hydrogen peroxide accumulation. This protective effect helps maintain cellular membrane integrity and ensures normal metabolic processes, ultimately supporting enhanced photosynthetic performance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2 The improvement of antioxidant capacity of rice leaves by silicon mainly depended on non-enzymatic antioxidant system\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNext, we independently studied the correlation between the antioxidative properties and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e of rice leaves under the regulation of silicon, in order to explore the key enzymes that play a role in antioxidant capacity. The results demonstrate that silicon (Si) regulation induces distinct antioxidant strategies in the two rice varieties, with both primarily utilizing the ascorbate-glutathione (ASA-GSH) cycle as their core non-enzymatic antioxidant system. However, notable varietal differences exist in their key enzymatic components. In XLY, the antioxidant response predominantly depends on galactono-1,4-lactone dehydrogenase (GalLDH), ascorbate oxidase (AAO), and glutathione reductase (GR). In contrast, QY employs a more comprehensive enzymatic network involving GalLDH, AAO, ascorbate peroxidase (APX), dehydroascorbate reductase (DHAR), and monodehydroascorbate reductase (MDHAR) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb and c). These findings reveal that while silicon similarly activates non-enzymatic antioxidant systems in both varieties, it triggers fundamentally different enzymatic regulation mechanisms.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Silicon increases rice yield and biomass accumulation and redistributes yield components\u003c/h2\u003e\n \u003cp\u003eFinally, we measured the economic yield and biological yield of rice in the pot experiment stage, and tested the seeds to explore its yield composition (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and Figure S3). The results showed that the Si-3 treatment showed the best results in both economic yield and biological yield, which was basically consistent with the net photosynthetic rate described above. However, yield and biomass are only one aspect of photosynthesis. For the pot experiment, quality measurement is not accurate, so we will repeat and verify the experiment in the field below to accurately explore the effect of silicon on rice quality and verify the conclusion that silicon improves rice yield\u003c/p\u003e\n \u003cp\u003eTo elucidate the relationship between photoassimilate allocation and yield formation, we systematically analyzed the correlation between net photosynthetic rate and yield components across different growth stages (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The results revealed distinct yield formation patterns between the two cultivars: for cultivar XLY, significant positive correlations were observed between photosynthetic activity and final biomass/yield at all growth stages, indicating that yield accumulation was achieved through balanced photoassimilate distribution throughout development. In contrast, cultivar QY exhibited a dynamic allocation pattern where the contribution of photoassimilate to yield gradually increased with plant development, while their contribution to biomass accumulation correspondingly decreased. Regarding yield component regulation, both cultivars showed similar trends: all yield components except grain filling rate (GSR) displayed relatively uniform accumulation patterns, while GSR progressively declined during grain development. These findings demonstrate that although the two cultivars differ in their photoassimilate partitioning strategies for biomass accumulation, they share common characteristics in yield component regulation, with GSR being the only key factor showing a consistent decreasing trend.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e also increased rice yield and improved rice quality in field condition\u003c/h2\u003e\n \u003cp\u003eIn order to get close to the production and verify the previous results, we carried out experiments and measured the yield in the field cultivation environment (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). In accordance with the results of pot experiment, Si-3 treatment was the most effective in increasing the yield of both rice varieties. This also means that the photosynthetic mechanism revealed in the pot experiment is relatively stable and reliable. In addition, we measured the quality of rice under field cultivation. In terms of processing quality, silicon significantly improved the yield of brown rice, milled rice and head rice rate, and the Si-3 treatment had the most significant improvement, and their values were increased by 8.6 (5.4) %, 7.8 (8.1) % and 8.4 (6.2) %, respectively, compared with the control group (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). In addition, the content of rice protein and the changes of various protein components were also determined. As shown in the figure, silicon significantly increased the total protein content in rice, among which Si3 treatment had the most significant effect, and its value was increased by 9.9 (11.2) % compared with the control group. Compared with the control group, the contents of albumin, prolamin and gluten in Si treated XLY rice were significantly increased by 15.0%, 16.3% and 10.3%, respectively, while globulin was decreased by 17.2%. Compared with Control the protein content of Si-3 rice in QY increased by 11.5% (Albumin), 14.7% (Globulin), 7.2% (Prolamin) and 11.4% (Glutelin), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). In addition, we also found that it can significantly improve the Chalkiness Degree and Chalky Grain Rate of rice, and also increase the 2-AP and AaC contents. At the same time, the AC content and AC/AaC were significantly reduced, and the changes were most significant after Si-3 treatment (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). In addition, considering the relatively complex results of rice RVA (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), with many positive and negative indicators, and the overall quality indicators, we conducted a comprehensive assignment weighted score for the quality indicators under each treatment based on principal component analysis, and the results were shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec. After comprehensive scoring, we found that the comprehensive quality of the two rice varieties showed a significant upward trend under the regulation of silicon, and the improvement of the two varieties was most significant under the treatment of Si-3.\u003c/p\u003e\n \u003cp\u003eIn order to further investigate the distribution of photocompound on rice quality, a structural equation model was established to investigate the effect of photocompound on rice quality under the control of silicon (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The results showed that the influence mechanism of photosynthate distribution under silicon control was basically the same for the two varieties. Photocontracides had positive effects on AaC content, 2-AP content and total protein content, but had negative effects on chalkness index and AC content. Comparing the two varieties, compared with QY, photocontractions focused on XLY in the aspects of Head Rice Rate, AaC Content, Chalkiness Degree and total protein, while other aspects focused more on QY.\u003c/p\u003e\n \u003cp\u003eSo far, we evaluated the photosynthetic capacity, antioxidant properties, yield and quality indexes of rice regulated by different amounts of silicon under artificial conditions and field conditions. The results showed that all indexes pointed to SI-3 treatment, that is, 0.75mM K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5The influence of exogenous silicon on the silicon content in rice\u003c/h2\u003e\n \u003cp\u003eThe silicon content in rice samples under different silicon treatment concentrations was successfully determined using ICP-MS, revealing a clear dose-dependent increase in silicon accumulation as the exogenous silicon application level rose from 0 to 1.25 mg/kg (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). The control group without silicon addition showed a low background silicon level, while at 0.25 mg/kg, the silicon content in the two biological replicates (\u003cem\u003eXLY\u003c/em\u003e and \u003cem\u003eQY\u003c/em\u003e) increased to 16.88 mg/kg and 15.32 mg/kg, respectively. As the treatment concentration reached 1.25 mg/kg, silicon accumulation peaked at 42.24 mg/kg for \u003cem\u003eXLY\u003c/em\u003e and 40.08 mg/kg for \u003cem\u003eQY\u003c/em\u003e, both significantly higher than the control. These results demonstrate a strong silicon enrichment capacity in rice and a notable positive correlation between silicon content and treatment concentration, confirming that exogenous silicon application can effectively modulate silicon levels in rice.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eRice, serving as a principal staple crop globally, plays a fundamental role in maintaining food security and supporting sustainable agricultural systems (Wu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Silicon (Si), widely acknowledged as a beneficial quasi-essential element for plant development, has shown remarkable capacity to improve photosynthetic performance, enhance mechanical strength (thereby reducing lodging risk), and boost stress tolerance across diverse crop species (Gulzar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite these recognized benefits, there remains a paucity of comprehensive studies examining the integrated effects of silicon on photosynthetic characteristics, antioxidant defense systems, yield potential, and grain quality parameters in rice. Understanding the mechanistic basis of silicon-mediated improvements in rice production carries substantial implications for addressing global food security challenges and advancing sustainable farming practices. In the present investigation, we implemented a foliar application approach using graded concentrations of potassium silicate (K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) to systematically evaluate its physiological and agronomic impacts. Through meticulous measurement of photosynthetic indices and antioxidant profiles at critical growth stages, coupled with thorough assessments of field performance and grain quality attributes, this study provides crucial insights for developing optimized silicon management protocols in rice cultivation, with the ultimate goal of simultaneously enhancing both productivity and grain quality.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Silicon can mobilize various photosynthetic mechanisms in rice leaves to promote photosynthesis collaboratively\u003c/h2\u003e\u003cp\u003ePrevious studies have well documented the positive effects of silicon on plant photosynthesis across various species. Zhang et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that silicon application significantly enhances the net photosynthetic rate and stress resistance in tomato leaves. Similar findings were reported in maize (Xie et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), cucumber (Hattori et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and banana (Asmar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, such systematic investigations remain scarce in rice plants. Our current study not only confirms the photosynthetic-promoting effect of silicon in rice, consistent with previous findings in other crops, but also reveals stage-specific regulatory mechanisms: during vegetative growth, silicon primarily enhances photosynthesis by modulating stomatal behavior, while post-flowering stages involve more complex photosynthetic pathway regulation.\u003c/p\u003e\u003cp\u003eAlthough silicon is not a structural component of photosynthetic pigments, it may indirectly increase pigment content by facilitating magnesium uptake (dos Santos et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which likely explains the elevated chlorophyll and carotenoid levels observed in our study. Additionally, silicon contributes to photosynthetic improvement through multiple mechanisms: (1) modifying cell wall architecture to improve stem erectness and light interception (Pan et al., 2024); (2) mediating stomatal regulation via hormonal pathways (Park et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as evidenced in our experimental results. These findings suggest that the enhanced photosynthetic capacity in rice leaves results from the synergistic effects of these multifaceted regulatory mechanisms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Silicon can mobilize the antioxidant system of rice plants to resist adverse external environments\u003c/h2\u003e\u003cp\u003eAntioxidant capacity plays a crucial role in plant growth and development. Previous studies have demonstrated that silicon can significantly enhance the antioxidant potential in plants, as shown by Xu et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in strawberry plants where silicon improved both yield and quality. Our findings reveal that silicon similarly activates both enzymatic and non-enzymatic antioxidant systems in rice plants. The antioxidant effects of silicon operate through multiple interconnected mechanisms. First, silicon deposits on cell walls to form a protective silicon-cellulose composite layer that enhances mechanical strength while reducing pathogen invasion and mechanical damage, thereby minimizing ROS generation under various stresses (Sanadhya et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, silicon promotes the biosynthesis of key antioxidants including glutathione (GSH), ascorbic acid (AsA), and phenolic compounds that directly neutralize ROS. Another important mechanism involves silicon's ability to chelate transition metals like Fe\u003csup\u003e2+\u003c/sup\u003e and Cu\u003csup\u003e+\u003c/sup\u003e, thereby inhibiting the ROS-generating Fenton reaction (Yang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, silicon contributes to antioxidant defense through heavy metal chelation and modulation of hormonal regulation systems (Rachappanavar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, the enhancement of the antioxidant system by silicon provides critical protection for photosynthetic apparatus, particularly under stress conditions. This protective effect represents a fundamental mechanism underlying the observed improvements in photosynthetic capacity, as the reinforced antioxidant system prevents oxidative damage to chloroplasts and maintains optimal photosynthetic function.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Silicon could have a positive effect on rice yield\u003c/h2\u003e\u003cp\u003eNumerous studies have demonstrated the yield-enhancing effects of silicon application in various crops. As evidenced by Dou et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in tomatoes and Artyszak et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in sugar beets, silicon supplementation consistently improves crop productivity. Our findings corroborate these observations, showing that silicon application significantly increases both grain yield and biomass accumulation in rice under both field conditions and controlled phenological cultivation. The yield improvement can be attributed to multiple physiological mechanisms: (1) enhanced root system development, where silicon promotes root formation and increases surface area, thereby improving the uptake of essential macronutrients including nitrogen, phosphorus, and potassium (Saja-Garbarz et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); (2) optimized plant architecture that facilitates better light interception and photosynthetic efficiency at the canopy level (Thakral et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); and (3) improved photosynthetic performance and antioxidant capacity as discussed previously. These synergistic effects collectively contribute to the observed increases in both economic yield (grain production) and biological yield (total biomass) in rice.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Silicon can comprehensively enhance the quality of rice\u003c/h2\u003e\u003cp\u003eCurrent understanding of silicon's role in regulating rice grain quality remains limited, largely due to the complexity of quality evaluation parameters and incomplete elucidation of the underlying physiological mechanisms. This study reveals that silicon application significantly enhances both the content and proportion of translucent endosperm in rice grains. These improvements may be attributed to silicon's ability to upregulate key enzymatic activities in the sucrose-to-starch conversion pathway, particularly during the critical grain-filling stage (Peng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The silicon-induced modifications in starch composition confer multiple quality enhancements: by reducing chalkiness formation and improving grain transparency, silicon elevates the appearance quality of rice. Simultaneously, it optimizes starch deposition patterns within the endosperm, thereby increasing grain structural integrity and subsequently reducing breakage rates during milling (Yan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., 2024). These findings provide novel insights into silicon fertilization as a viable strategy for concurrently improving both milling yield and visual quality characteristics in rice production systems.\u003c/p\u003e\u003cp\u003eEmerging evidence highlights silicon's multifaceted role in enhancing rice grain quality through physiological and biochemical regulation. Notably, silicon facilitates protein accumulation in grains by improving nitrogen uptake and translocation, while simultaneously modulating the gluten-to-prolamin ratio and increasing essential amino acid content, particularly lysine, thereby elevating the nutritional profile of rice. This synergistic effect proves especially pronounced under nitrogen-limited conditions (Yamaji et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Concurrently, silicon sustains photosynthetic capacity during grain filling by boosting the activity of key antioxidant enzymes (SOD, POD) in flag leaves, effectively delaying leaf senescence and promoting more complete, uniform grain development - fundamental prerequisites for superior milling and appearance quality (Mushtaq et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Perhaps most remarkably, silicon influences secondary metabolic pathways to enhance biosynthesis of characteristic aroma compounds, including 2-acetyl-1-pyrroline, ultimately improving the aromatic qualities of rice (Praseartkul et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this study demonstrates that potassium silicate (K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) application significantly enhances rice productivity and quality through multiple physiological mechanisms. The key findings can be summarized as follows:\u003c/p\u003e\u003cp\u003eFirst, K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e improves photosynthetic performance across all growth stages by regulating both stomatal conductance and electron transport systems, as evidenced by enhanced Fv/Fm, qP, ETR values, photosynthetic pigment content, and key enzyme activities. Second, it strengthens the plant's antioxidant defense system through coordinated activation of both enzymatic (CAT, SOD) and non-enzymatic (ASA-GSH cycle) components. Third, K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e treatment optimizes yield components, leading to significant increases in both grain yield and total biomass accumulation.\u003c/p\u003e\u003cp\u003eNotably, K\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e application comprehensively improves multiple grain quality parameters, including processing characteristics, appearance, palatability, nutritional value (through increased essential amino acids), and aromatic compound content. Among the tested concentrations, the 0.75 mM pure silicon treatment (Si-3) showed the most pronounced effects across all measured parameters, suggesting its optimal potential for practical application in rice production systems to simultaneously enhance both yield and quality.\u003c/p\u003e\u003cp\u003eThese findings provide strong evidence for incorporating silicon fertilization as an effective agronomic practice in rice cultivation systems aiming for improved productivity and premium grain quality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by the Hubei Provincial Natural Science Foundation Young Project (JCZRQN202401143).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArtyszak A, Gozdowski D, Kuchinka K (2015) Foliar nutrition effectiveness for sugar beet cultivated as a following crop after winter rape. 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PLANT SOIL ENVIRON 67:633\u0026ndash;642. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17221/217/2021-PSE\u003c/span\u003e\u003cspan address=\"10.17221/217/2021-PSE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Silicon, Photosynthesis, Rice, Quality, yield: Antioxidation","lastPublishedDoi":"10.21203/rs.3.rs-7606764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7606764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e\u003cp\u003eSilicon is increasingly recognized as a beneficial element for rice growth, yet limited research has explored how it regulates photosynthesis to influence yield and quality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThrough pot experiments and three years of field validation, this study systematically investigated the effects of different silicon concentrations on photosynthetic characteristics throughout the entire growth cycle of rice, as well as on yield and quality parameters.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe results show that silicon application significantly increased the net photosynthetic rate of leaves at all growth stages and optimized photosynthetic parameters (elevated Fv/Fm, Fv'/Fm', qP, Y(II), and ETR; reduced NPQ). It also enhanced photosynthetic pigment content, improved photosynthase activity and membrane integrity. Additionally, silicon activated the antioxidant defense system, boosting the activity of antioxidant enzymes (CAT and SOD) and stimulating the ASA-GSH cycle, thereby comprehensively enhancing antioxidant capacity. Under field conditions, silicon application significantly increased grain yield and biomass yield while improving quality metrics: reduced chalkiness, optimized starch content and composition, and enhanced processing quality and nutritional value. Notably, silicon treatment increased the content of key aromatic compounds (particularly 2-AP), leading to an overall improvement in quality. These findings indicate that silicon improves yield and quality by enhancing photosynthetic efficiency and strengthening the antioxidant system, with the most pronounced effects observed at 0.75 mM pure silicon (applied as H\u003csub\u003e2\u003c/sub\u003eSi\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe study suggests that strategic silicon application can be an effective approach to ensuring food security and promoting sustainable development in the rice industry.\u003c/p\u003e","manuscriptTitle":"From Photosynthesis to Antioxidants: How Silicon (K₂Si₂O₅) Improves Yield and Grain Quality in Rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 19:55:07","doi":"10.21203/rs.3.rs-7606764/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c2435c0-1458-4ef0-9b8a-3b4426cd5d10","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-31T02:40:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 19:55:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7606764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7606764","identity":"rs-7606764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}