Cleaner Engineering and Technology最新文献

{"title":"Performance of natural rubber latex modified bottom ash-based geopolymer stabilized recycled concrete aggregate as a pavement base material","authors":"Chokchai Traiyasut ,&nbsp;Menglim Hoy ,&nbsp;Suksun Horpibulsuk ,&nbsp;Apichat Suddeepong ,&nbsp;Apinun Buritatum ,&nbsp;Teerasak Yaowarat ,&nbsp;Artit Udomchai ,&nbsp;Arul Arulrajah ,&nbsp;Avirut Chinkulkijniwat ,&nbsp;Punvalai Choenklang","doi":"10.1016/j.clet.2025.101080","DOIUrl":"10.1016/j.clet.2025.101080","url":null,"abstract":"<div><div>The present research assessed the performance of natural rubber latex (NRL) modified bottom ash (BA)-based geopolymer stabilized recycled concrete aggregate (RCA) as a sustainable pavement base material. Effects of NRL content (0.1, 0.2 %, and 0.3 % by weight of aggregate) and alkaline activator ratios (NaOH:Na₂SiO₃, G/N = 1:1, 1:1., and 1:2) in strength development and microstructure of the stabilized mixtures were evaluated. Unconfined compressive strength (UCS) tests were carried out at 7 and 28 days of curing, and the results were correlated with microstructural analyses using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). The findings revealed that the mixture containing 0.1 % NRL and a G/N ratio of 1:1 exhibited the highest UCS values of 4.0 MPa and 5.17 MPa at 7 and 28 days respectively, surpassing the minimum strength requirement for pavement subbase materials. The microstructural analyses confirmed the constitution of a dense and homogeneous geopolymer matrix, with NRL films acting as bridging structures that enhanced the bonding between RCA particles and the matrix. Higher ratios of G/N led to a silica-rich gel that hindered further geopolymerization and strength gain. Meanwhile, a higher ratio of NRL resulted in the formation of thicker NRL films that interfered with the geopolymerization process, resulting in a subsequent strength reduction. The study demonstrates the potential of NRL-modified BA-based geopolymer stabilized RCA as an environmentally friendly and high-performance alternative to conventional cement-stabilized pavement base materials.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101080"},"PeriodicalIF":6.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward sustainable construction 3D printing: limestone and non-calcined recycled marine clay as partial cement replacement","authors":"Harn Wei Kua,&nbsp;Anqi Shi ,&nbsp;Vignesh Kajandran,&nbsp;Charlene,&nbsp;Tiam Weng Mark Lam,&nbsp;Abhimanyu Goel,&nbsp;De Hui Alwin Wong,&nbsp;Peak Kee Lim,&nbsp;Layla Harris Kasee,&nbsp;Xi Wen Ong,&nbsp;Ghasan Fahem Huseien,&nbsp;Alexander Lin","doi":"10.1016/j.clet.2025.101074","DOIUrl":"10.1016/j.clet.2025.101074","url":null,"abstract":"<div><div>This study investigates the effects of partial substitution of Ordinary Portland Cement with different proportions of non-calcined marine clay and different grades (by particle size) of Ground Limestone to formulate 3D-printable concrete. Non-calcined clay was used because of its lower energy requirement than calcined clay. The rheology of the mixes was evaluated by extrudability, tack, Large Amplitude Oscillatory shear, and Logarithmic Stress Ramp tests. Their mechanical performance was evaluated with respect to compressive, splitting, shear, and three-point bending (flexural) strengths. Results show that adding only about 1 % of oven-dried (low temperature) marine clay and 19 % of limestone improve cohesion and build-up of static yield strength while ensuring extrudability. This study hopes to pave the way for more studies on using non-calcined marine clay as a more sustainable option for construction 3D printing.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101074"},"PeriodicalIF":6.5,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bio-hydrogel formulation for co-immobilization of microalgae and bacteria in living biofilters for nutrient recovery from secondary industrial effluents","authors":"Chalampol Janpum ,&nbsp;Jagroop Pandhal ,&nbsp;Nuttapon Pombubpa ,&nbsp;Tanakit Komkhum ,&nbsp;Chonnikarn Sirichan ,&nbsp;Piyakorn Srichuen ,&nbsp;Pichaya In-na","doi":"10.1016/j.clet.2025.101075","DOIUrl":"10.1016/j.clet.2025.101075","url":null,"abstract":"<div><div>The increasing discharge of nutrient-rich industrial effluents poses a significant environmental challenge, necessitating efficient and sustainable wastewater treatment strategies. This study developed a living hydrogel-based biofilter incorporating co-immobilized <em>Chlorella</em> sp. and <em>Bacillus subtilis</em> TISTR 1415 to enhance nutrient recovery from secondary industrial effluent from vegetable oil factories. Hydrogels were formulated using guar gum and carrageenan, crosslinked with potassium chloride (KCl), and evaluated for their stability and microbial immobilization efficiency. Among the tested formulations, the hydrogel with 0.3 M KCl exhibited optimal properties, including moderate swelling capacity (∼1,005 % or ∼10 g_water/g_{dry hydrogel}), reduced solubility (∼40 %), and enhanced mechanical stability and crosslinking density, leading to improved porosity and microbial retention. These physicochemical properties facilitated efficient nutrient diffusion and sustained cell viability within the hydrogel matrix. The synthetic co-culture biofilter with a 3:1 ratio of <em>Chlorella</em> sp. to <em>B. subtilis</em> significantly enhanced nutrient removal efficiencies compared to monocultures, achieving 98.68 % ammonium (NH₄⁺), 53.45 % phosphate (PO₄³⁻), and 68.60 % COD removal over 7-day trials. The synergistic interaction between microalgae and bacteria facilitated improved nutrient uptake, organic matter degradation, and enhanced effluent treatment performance. Furthermore, pH and dissolved oxygen levels were significantly influenced by microbial activity, with microalgae contributing to oxygen production and pH elevation, while bacteria aided organic matter breakdown. The living hydrogel-based biofilter presents a promising alternative to conventional wastewater treatment methods by harnessing the synergistic interactions between biological processes and hydrogel immobilization technology. This approach enhances effluent quality and contributes to innovative solutions for environmental protection and nutrient recovery.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101075"},"PeriodicalIF":6.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High recovery of anhydrous cement in dried concrete slurry waste for use as supplementary cementitious material in low-CO2 concretes","authors":"Daniel O.F. Silva ,&nbsp;Valdir M. Pereira ,&nbsp;Antônio C.V. Coelho ,&nbsp;Sérgio C. Angulo","doi":"10.1016/j.clet.2025.101076","DOIUrl":"10.1016/j.clet.2025.101076","url":null,"abstract":"<div><div>Concrete slurry waste (CSW) is a by-product generated from returned concrete and the mixer truck washing process, accounting for approximately 3–5% of total concrete production. Although various strategies for recycling CSW have been developed, large-scale recovery of its anhydrous cement fraction, such implemented in the present study, has not been previously reported. Moreover, prior studies have focused almost exclusively on the use of low-reactivity CSW as a supplementary cementitious material (SCM). In this study, a filtering and rapid drying procedure was applied to CSW upon its return to the ready-mixed concrete plant (RMCP). The influence of the recovery time and type of concrete waste on the preservation of the anhydrous cement fraction was evaluated. The recovered material was subsequently used to partially replace Portland cement in cementitious paste formulations. The samples were characterized using analytical methods, such X-ray fluorescence, HCl leaching assay, thermogravimetric analysis, isothermal calorimetry, and quantitative X-ray diffraction (QXRD). The results demonstrated that the anhydrous cement content in CSW was significant, approximately 30% by mass, due to the efficiency of the filtering and rapid drying process. Furthermore, the time exposure to water had no notable effect on the preserved anhydrous cement fraction. Three CSW samples were selected and incorporated into cement pastes, replacing 35 %–75% (by mass) of Portland cement. The resulting pastes exhibited mechanical strength values comparable to, or statistically equivalent to, those of the reference paste made with 100 % Portland cement. The recovery methodology has potential for the development of zero-waste ready-mix concrete plants, and the low emission concrete formulation proposed in this study enabled a reduction of up to 55% in specific CO₂ emissions. This approach could reduce Portland cement consumption by approximately 15% (by mass) in ready-mix operations, contributing significantly to sustainability in the concrete industry.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"29 ","pages":"Article 101076"},"PeriodicalIF":6.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the environmental impacts of nanocellulose production using conventional and novel approach at laboratory scale","authors":"Nishtha Talwar ,&nbsp;Oscar Huerta ,&nbsp;Daniela Millán ,&nbsp;Paulina Pavez ,&nbsp;Mauricio Isaacs ,&nbsp;Nicholas M. Holden","doi":"10.1016/j.clet.2025.101063","DOIUrl":"10.1016/j.clet.2025.101063","url":null,"abstract":"<div><div>Green chemistry promotes the design and application of chemical products and processes that reduce or preferably eliminate the use and generation of hazardous substances. The objective of this research was to evaluate the environmental performance of two methods of producing nanocellulose at the laboratory scale: (i) a conventional sulphuric acid hydrolysis that has been upscaled for industrial use (TRL 8–9); and (ii) the novel approach using the ternary eutectic mixture ChCl: pTSA: PA molar ratio (1:1:1.35) that is currently at TRL 2. The purpose of developing the new approach was to find a better alternative to the conventional process from an environmental perspective. To validate this, life cycle assessment was used to compare conventional vs novel methods with the functional unit of 1 g nanocellulose produced. The system boundary was from cradle to laboratory gate. The results were interpreted to select the best method for laboratory use and to identify design issues to address during upscaling of the novel method. For both methods, conventional and novel, the impact categories selected were climate change (kgCO2 eq), Acidification (kg SO2 eq), Ecotox Air (CTUe) and Eutrophication (kg N eq). To produce 1 g of nanocellulose with sulphuric acid caused a climate impact of between 68 kg CO2 eq (90 % yield) to 105 kg CO2 eq (57 % yield). Produced using DES the climate impact ranged from 85 kg CO2 eq.(90 % yield) to 132 kg CO2 (57 % yield). The results indicated that the novel method created greater impacts over the whole life cycle. Unless significant changes are made during upscaling, the novel method will not make a positive contribution to sustainable, circular bioeconomy. The method does have potential to be improved to reduce impact, including using decarbonised energy, a renewable, bio-based feedstock for the cellulose and choline chloride to improve the overall efficiency of using deep eutectic solvent (DES) at pilot scale. The low TRL life cycle assessment offered insights not possible if only the laboratory stage of the analysis had been considered.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101063"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven energy optimization enhancing efficiency in urban environments with hybrid machine learning models","authors":"Ali Majnoon ,&nbsp;Amirali Saifoddin","doi":"10.1016/j.clet.2025.101072","DOIUrl":"10.1016/j.clet.2025.101072","url":null,"abstract":"<div><div>Accurate forecasting of electricity consumption is essential for sustainable urban planning, particularly in fast-growing cities like Tehran. Conventional models often fail to adequately capture the intricate relationships between environmental factors and energy demand. To overcome these limitations, this study applies advanced AI techniques such as Neural Networks, Random Forest Regression, and Gradient Boosting, using a comprehensive dataset (2000–2022) that integrates meteorological, environmental, and fuel consumption variables to enhance predictive performance. Random Forest Regression achieved the highest accuracy, with an R² 0.9835 and MSE of 0.0165, explaining 98.35 % of the variation in electricity consumption. Feature engineering substantially improved model accuracy, highlighting temperature variables (T2M, T2M_MAX, T2M_MIN) and fuel consumption as the most influential predictors. Correlation analysis revealed strong associations between environmental factors and electricity demand. Using Sequential Least Squares Programming (SLSQP) optimization, the study determined conditions that reduced electricity consumption to 1.09 million kWh. These findings highlight the value of AI models in enhancing forecasting accuracy and supporting efficient energy planning. Ensemble learning and optimization methods strengthen sustainable energy management. However, reliance on historical data and neglect of socio-economic factors may constrain the models’ adaptability and predictive power. Moreover, the complexity of AI models presents interpretability challenges, requiring additional efforts to align outputs with policy-making needs. Leveraging AI and data-driven methods, this study offers actionable insights for policymakers to optimize energy use and curb emissions in urban settings like Tehran. Future research should incorporate socio-economic variables and hybrid models to enhance predictive reliability and practical relevance.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101072"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven modeling using machine learning to investigate the desulfurization performance by zeolitic adsorbents","authors":"Mahyar Mansouri,&nbsp;Mohsen Shayanmehr,&nbsp;Ahad Ghaemi","doi":"10.1016/j.clet.2025.101073","DOIUrl":"10.1016/j.clet.2025.101073","url":null,"abstract":"<div><div>This work introduces an experimentally validated, data-driven machine learning (ML) framework for predicting the adsorptive desulfurization (ADS) performance of zeolite-based materials. A curated dataset of 700 entries was compiled from diverse sources, incorporating key structural and operational parameters such as Brunauer–Emmett–Teller (BET) surface area, total pore volume (TPV), temperature, contact time, and molecular weight of sulfur compounds (MW-S). Seven ML models were developed and compared, with Extra Trees Regressor (ETR) achieving the best performance (R² = 0.9979, MAE = 0.0308), followed by Random Forest (RF) (R² = 0.9932, MAE = 0.0524). Feature importance analysis and shapley additive explanations (SHAP) identified molecular weight and BET surface area as the most influential descriptors. For better interpretability and generalizability, the zeolite type was excluded as an input feature and replaced by physicochemical properties. Furthermore, the top-performing model was integrated with a genetic algorithm (GA) to optimize operating conditions, resulting in a predicted maximum adsorption capacity of 131.63 mg S/g. Model robustness was also confirmed using an independent test set. Overall, this study provides a reliable and interpretable framework for accelerating ADS system design and can be extended to other adsorption-based separation processes.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101073"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the barriers to hydrogen fuel cell vehicles adoption in the Gulf-Europe corridor: a Fuzzy AHP and ISM analysis","authors":"Md. Habibur Rahman ,&nbsp;Roberto Baldacci ,&nbsp;Carlos Méndez ,&nbsp;Md Al Amin","doi":"10.1016/j.clet.2025.101069","DOIUrl":"10.1016/j.clet.2025.101069","url":null,"abstract":"<div><div>The adoption of hydrogen fuel cell vehicles (HFCVs) is essential for achieving sustainable, low-carbon transportation, but many barriers hinder this transition. Therefore, this study aims to identify, categorize, and prioritize these barriers in the context of the Gulf-Europe corridor, also known as the Iraq Development Road Project (DRP). To achieve this, we adopt a two-stage methodological framework that integrates the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) to quantify the relative importance of thirty secondary barriers, and Interpretive Structural Modeling (ISM) to explore the interdependencies among the top ten. The Fuzzy AHP results highlight technological, economic, and infrastructure-related barriers as the most critical primary barriers. The ISM analysis further reveals that three barriers, lack of hydrogen production hubs, limited hydrogen transport options, and hydrogen storage and transportation, are independent. Six barriers, fuel cell efficiency and durability, hydrogen production and distribution costs, vehicle range and refueling time, infrastructure investment, refueling station compatibility issues, and hydrogen purity requirements, are classified as linkage barriers. One barrier, high initial vehicle cost, is found to be dependent. To accelerate HFCVs adoption, we recommend strengthening hydrogen infrastructure, fostering technological innovation, reducing costs through targeted incentives, and enhancing policy coordination among stakeholders and policymakers. This study contributes to literature by offering a comprehensive understanding of the adoption barriers and providing actionable insights to support the development of more effective strategies. Notably, it uniquely addresses social, logistical, and technological barriers, alongside geographic barriers, that have been largely overlooked in previous studies.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101069"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting hydrogen production through electrolysis powered by concentrated solar power plant using artificial neural network","authors":"Hanane Ait Lahoussine Ouali ,&nbsp;Otman Abida ,&nbsp;Mohamed Essalhi ,&nbsp;Nisar Ali ,&nbsp;Ibrahim Moukhtar","doi":"10.1016/j.clet.2025.101071","DOIUrl":"10.1016/j.clet.2025.101071","url":null,"abstract":"<div><div>In today's world, artificial intelligence has become a vital utility technology with the potential to benefit various industries and research endeavors significantly. One such application lies in harnessing solar thermal energy for green hydrogen purposes. Hence, this study aims to examine the feed-forward back-propagation network (FFBPN) in the context of a Dish/Stirling powered electrolysis system for hydrogen production, utilizing time series data from over twenty locations in Morocco. The FFBPN model was developed to evaluate the impact of various input parameters, including direct normal irradiation (DNI) at different geographical locations, on hydrogen production from the Dish/Stirling powered electrolysis system. This model was trained using different training algorithms, namely Levenberg-Marquardt (LM), Fletcher-Powell Conjugate Gradient (CGF), One Step Secant (OSS), and Scaled Conjugate Gradient Back-propagation (SCG), to identify the most effective approach for predicting green hydrogen production. By using a variety of training algorithms and evaluating the models using specified metrics, the study aimed to determine the most suitable and effective training approach for the given data. The different results indicate that, among all the locations examined in eastern Morocco, Figuig and Bouarfa cities have been identified as the most appropriate locations for implementing the proposed system, yielding the highest annual net electric energy output of 83.52 GWh/yr and 81.92 GWh/yr, respectively. Furthermore, the system allowed the production of over 1462 tons/yr of green hydrogen, supported by a total installed capacity of 50 MWe. Furthermore, the statistical analysis reveals that the Levenberg-Marquardt (LM) algorithm, using 33 neurons, outperformed others, exhibiting the lowest errors and the highest R² value during both training and testing. Specifically, during training, the metrics of RMSE, MRE, COV, and R² recorded values of 0.582, 0.395, 0.510, and 0.99999, respectively, whereas during testing they were 0.633, 0.474, 0.560, and 0.99999, respectively. The FFBPN application stands as a pioneering and effective model to predict green hydrogen production from the Dish/Stirling system.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101071"},"PeriodicalIF":6.5,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Silicon-functionalized nanotherapeutics modulate physio-biochemical functions and soil enzyme profile for curtailing cadmium toxicity in rice (Oryza sativa L.) at vegetative phase","authors":"Munazza Ijaz ,&nbsp;Rafia Ijaz ,&nbsp;Ji'an Bi ,&nbsp;Temoor Ahmed ,&nbsp;Muhammad Noman ,&nbsp;Humera Rani ,&nbsp;Muhammad Babar Malook ,&nbsp;Muhammad Shafiq Shahid ,&nbsp;Gabrijel Ondrasek ,&nbsp;Baoyi Lin ,&nbsp;Bin Li","doi":"10.1016/j.clet.2025.101070","DOIUrl":"10.1016/j.clet.2025.101070","url":null,"abstract":"<div><div>Cadmium (Cd) contamination severely threatens agricultural productivity and food safety. This study examines the ability of biogenic silicon nanoparticles (SiNPs) as nanotherapeutics to mitigate Cd stress in rice (<em>Oryza sativa</em> L.) by enhancing physiological and biochemical responses. A controlled greenhouse experiment demonstrated that SiNPs (250 mg kg⁻¹) significantly improved plant growth under Cd stress. The application of SiNPs increased plant height, fresh and dry weight by 22.98 %, 25.18 %, and 30.01 %, respectively, as compared to the control. Photosynthetic efficiency was also improved, as evidenced by increase in chlorophyll <em>a</em> and <em>b</em> content (17.02 % and 56.86 %, respectively). SiNPs strengthened the plant defense system by enhancing the activities of antioxidant enzymes, such as superoxide dismutase (23.18 %), peroxidase (41.98 %), and ascorbate peroxidase (11.29 %), while simultaneously reducing reactive oxygen species accumulation. SiNPs also enhanced the absorption of various essential nutrients and reduced Cd accumulation (by 61.04 %) in rice leaves compared to Cd-stressed plants without SiNPs treatment. Gene expression analysis showed that SiNPs upregulated genes associated with silicon transport, antioxidant activity, and phyto-chelation, further validating the Cd detoxification in rice plants. Moreover, soil enzyme activities and nutrient cycling improved upon SiNPs exposure. Leaf ultrastructure analysis revealed that SiNPs preserved normal cellular morphology and minimized Cd-induced damage. These findings highlight biogenic SiNPs (as nanotherapeutics) are effective and environmentally friendly solution for reducing Cd toxicity in rice.</div></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"28 ","pages":"Article 101070"},"PeriodicalIF":6.5,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}