Nanoscale Research Letters最新文献

{"title":"Engineering L-Arg@ZIF-8 nanoparticles to modulate the immune–osteogenic–angiogenic microenvironment for accelerated bone fracture healing","authors":"Siyu Chen,&nbsp;Taojin Feng,&nbsp;Jiang Liu,&nbsp;Yi Li,&nbsp;Yuqi Wang,&nbsp;Taiqian Gong,&nbsp;Yang Liu","doi":"10.1186/s11671-026-04603-9","DOIUrl":"10.1186/s11671-026-04603-9","url":null,"abstract":"<div><p>Bone fracture healing requires coordinated regulation of osteogenesis, angiogenesis, and immune homeostasis within a dynamically evolving microenvironment. However, current biomaterials rarely integrate these three regulatory dimensions. Herein, we engineered L-arginine–loaded zeolitic imidazolate framework-8 (L-Arg@ZIF-8) nanoparticles to remodel the immune–osteogenic–angiogenic microenvironment and accelerate fracture healing. L-Arg@ZIF-8 nanoparticles exhibited uniform morphology, positive surface charge, and stable L-Arg incorporation, enabling efficient cellular uptake and sustained bioactivity. Using two physiologically relevant 3D co-culture spheroid models (osteoblast–macrophage and osteoblast–endothelial), we demonstrated that L-Arg@ZIF-8 promotes osteogenic differentiation, endothelial activation and the upregulation of angiogenic markers, and strengthens osteogenesis–angiogenesis coupling. Bulk transcriptomic profiling further revealed activation of regenerative pathways, including PI3K–Akt and Wnt signaling, along with coordinated modulation of cytokine–receptor interactions and immune-related remodeling programs. In vivo, L-Arg@ZIF-8 markedly accelerated fracture repair in a rat rib fracture model, characterized by enhanced callus formation, increased bone mineral density, greater trabecular thickness, and a significantly elevated mineral apposition rate. Histological and immunofluorescence analyses confirmed upregulation of key osteogenic markers (OPN, OSX) at the fracture site. Together, these findings demonstrate that L-Arg@ZIF-8 functions as a bioengineered microenvironment-modulating nanoplatform that orchestrates immune regulation, osteogenesis, and angiogenesis to promote efficient fracture healing. This strategy offers a promising therapeutic avenue for translational management of complex fractures.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04603-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal transport in single, binary and tri-hybrid nanofluid over porous medium for heat and aeronautical applications","authors":"S. V. Padma,&nbsp;M. Sanjalee,&nbsp;Seethi Reddy Reddisekhar Reddy,&nbsp;Shaik Jakeer","doi":"10.1186/s11671-026-04573-y","DOIUrl":"10.1186/s11671-026-04573-y","url":null,"abstract":"<div><p>This study examines the flow and heat transfer behavior of a tri-hybrid nanofluid over an exponentially stretching/shrinking surface, incorporating the influences of thermal radiation, viscous dissipation, internal heat generation, and magnetic fields. The working fluid consists of a suspension of Al₂O₃, Cu, and TiO₂ nanoparticles in water. Governing partial differential equations are reduced to an ordinary differential system through similarity transformations, subject to prescribed surface temperature (PST) and prescribed heat flux (PHF) conditions. The transformed equations are solved numerically using MATLAB’s bvp4c solver. Parametric effects on velocity, thermal characteristics, and Nusselt number are analysed and illustrated via tables and graphs for both PST and PHF cases. Comparative analysis highlights the efficiency of the tri-hybrid nanofluid Al₂O₃-Cu-TiO₂-H₂O over single and binary nanofluids. Results show enhanced heat transfer with increasing magnetic parameter, supporting applications in thermal and aeronautical engineering, including heat exchangers, solar absorbers, and lightweight cooling systems.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04573-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced physical modeling approaches for high-precision TCAD simulation of GaN HEMT power devices: a review","authors":"Haocheng Zhao,&nbsp;Amirul Firdaus,&nbsp;Muhammad Farizuan,&nbsp;Weng-Hooi Tan,&nbsp;Hiroshi Kawarada,&nbsp;Shaili Falina,&nbsp;Mohd Syamsul","doi":"10.1186/s11671-026-04571-0","DOIUrl":"10.1186/s11671-026-04571-0","url":null,"abstract":"<div><p>High-electron-mobility transistors (HEMTs) based on wide bandgap (WBG) materials like gallium nitride (GaN) are vital for next-generation power electronics and high-frequency applications, offering high breakdown voltage, electron mobility, and power density. The global shift toward electrification and sustainability is driving demand for GaN and silicon carbide (SiC) power devices. However, challenges such as current collapse and increased channel resistance under high-power conditions hinder performance. To address these limitations, numerous solutions have been explored, with simulation emerging as an indispensable starting point. Technology Computer-Aided Design (TCAD) simulations play a critical role by enabling accurate modeling, performance optimization, and reduced experimental effort. This paper reviews key and advanced physical models in TCAD simulations of GaN HEMTs, covering mechanisms such as carrier transport, thermal effects, and impact ionization. Mobility models—FLDMOB, Albrecht, Gansat, Yamaguchi, Brooks-Herring, and Conwell-Weisskopf—are analyzed for capturing velocity saturation and nonlocal transport. Recombination models like Shockley-Read-Hall and Auger are discussed in relation to carrier lifetime, while impact ionization models, including van-Overstraeten-de-Man, Selberherr, and Okuto-Crowell, are evaluated for breakdown prediction. Emphasis is placed on choosing models suited to specific structures and conditions to ensure simulation accuracy. Advanced modeling enhances TCAD’s predictive power, supporting innovation in GaN-based power electronics.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04571-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nano-priming of lettuce seeds using Chitosan Stabilized iron oxide nanoparticles with tunable interfacial properties","authors":"Nurfarwizah Adzuan Hafiz,&nbsp;Anca Awal Sembada,&nbsp;Noor Fitrah Abu Bakar,&nbsp;Mohamad Sufian So’aib,&nbsp;I. Wuled Lenggoro,&nbsp;Mohamed Syazwan Osman","doi":"10.1186/s11671-026-04594-7","DOIUrl":"10.1186/s11671-026-04594-7","url":null,"abstract":"<div><p>Polymer molecular weight plays a central role in determining the balance between electrostatic and steric stabilization in polymer-coated colloidal systems, yet its influence on nanoparticle accessibility at soft, hydrated interfaces remains insufficiently resolved. Here, we examine the effect of chitosan molecular weight (1000, 10,000, and 50,000 g/mol) on the electrosteric stabilization and colloidal behavior of iron oxide nanoparticles. Nanoparticles stabilized with low-molecular-weight chitosan exhibit smaller hydrodynamic diameters (≈ 110 nm) and higher positive zeta potentials (≈ + 35 mV), leading to enhanced suspension stability and slower sedimentation relative to higher-molecular-weight formulations. Increasing chitosan molecular weight produces thicker polymer layers that enhance steric contributions while partially screening surface charge, resulting in larger hydrodynamic sizes (≈ 150–160 nm) and reduced colloidal stability. These trends indicate a molecular-weight-dependent transition from electrostatic-dominated to steric-dominated stabilization. Using hydrated seed coats as polysaccharide-rich soft interfaces to probe interfacial accessibility, micro–X-ray fluorescence mapping revealed enhanced nanoparticle surface association under conditions of optimal electrosteric balance. This interfacial behaviour correlated with increased germination responses (up to ≈ 92%) without detectable adverse physiological effects. Collectively, these results demonstrate that chitosan molecular weight provides a tunable parameter for controlling electrosteric stabilization and nanoparticle accessibility at soft interfaces.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04594-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical analysis of MHD Jeffrey hybrid nanofluid flow over a solar curved sheet using ANN model","authors":"Seethi Reddy Reddisekhar Reddy,&nbsp;P. Vijayalakshmi,&nbsp;Maduru Lakshmi Rupa,&nbsp;Sai Avanthika,&nbsp;A. Maanav,&nbsp;Shaik Jakeer","doi":"10.1186/s11671-026-04575-w","DOIUrl":"10.1186/s11671-026-04575-w","url":null,"abstract":"<div><p>The conversion of solar radiation into thermal energy has gained increasing attention due to the growing demand for renewable sources of heat and electricity. Nanofluids, owing to their enhanced heat transfer capabilities, play a significant role in improving the efficiency of solar thermal systems. In this study, the flow of silicone oil containing diamond and silicon dioxide nanoparticles over a curved extended permeable sheet is investigated in the presence of Darcy–Forchheimer porous medium, thermal radiation, and Lorentz force. The non-Newtonian behavior of the working fluid is modeled using the Jeffrey fluid model. The governing flow equations are transformed into ordinary differential equations (ODEs) and solved numerically using the MATLAB bvp4c solver. Furthermore, an intelligent computational approach based on the Levenberg–Marquardt algorithm combined with a multilayer perceptron (MLP) feed-forward backpropagation artificial neural network is employed. The effects of key parameters, including the Deborah number, injection/suction parameter, permeability parameter, Forchheimer number, Hartmann number, curvature parameter, Prandtl number, Eckert number, and heat generation/absorption parameter, are analyzed in terms of pressure, velocity, temperature, and heat transfer rate. The results indicate that porous resistance and magnetic effects significantly influence boundary layer formation and heat extraction efficiency, showing that optimally adjusted hybrid nanofluids can greatly enhance thermal transfer in porous solar collectors and curved absorber surfaces, thus providing valuable insights for the design of advanced solar thermal systems.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04575-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal and solutal analysis of oxytactic microbes in bioconvection slip flow of trihybrid nanofluid with activation energy using artificial neural network","authors":"Munawar Abbas,&nbsp;Md. Mahbub Alam,&nbsp;Abdulbasit A. Darem,&nbsp;Riadh Marzouki,&nbsp;Asma A. Alhashmi,&nbsp;Tareq M. Alkhaldi","doi":"10.1186/s11671-026-04563-0","DOIUrl":"10.1186/s11671-026-04563-0","url":null,"abstract":"<div><p>In this work, the intelligent Levenberg–Marquardt optimization approach is applied to evaluate the activation energy influence on thermo-bioconvection flow of a trihybrid nanofluid including oxytactic microbes via a plate using integrated numerical computation. This idea is frequently applied in industrial and bioengineering operations where complicated fluid conditions and microbial activity interact. The addition of oxytactic microorganisms and a trihybrid nanofluid allows the model to simulate bioconvection behavior relevant to wastewater treatment, biofuel generation, and bioreactors, all of which require efficient mixing and oxygen supply. It is more advantageous to improve biochemical reactions, microbial growth conditions, and nutrient distribution by using activation energy and ANN-based predictive analysis. As a result, the model makes it easier to create and refine advanced biotechnological systems, environmental monitoring setups, and microfluidic devices that use microbe-nanofluid interactions. The algorithm’s reliability is further confirmed using histogram and function fitness. For fluid dynamics, numerical approaches and artificial neural networks work well together, potentially leading to new discoveries in a range of domains. The findings of this study could help optimize fluid systems, increasing production and efficiency in a range of technological domains. As the bioconvection Schmidt number increases, the oxytactic microbe profile decreases.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04563-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal analysis of bioconvection flow of CNTs/water based hybrid nanofluid with gyrotactic microbes using artificial neural network","authors":"Mohamed Bechir Ben Hamida,&nbsp;Munawar Abbas,&nbsp;Ali Akgül,&nbsp;Murad Khan Hassani","doi":"10.1186/s11671-026-04495-9","DOIUrl":"10.1186/s11671-026-04495-9","url":null,"abstract":"<div><p>This study uses the Levenberg–Marquardt strategy with feed forward neural networks (LMS-FNN) to inspect the Soret-Dufour effect on radiative hybrid nanofluid flow across a Riga plate with gyrotactic microorganisms. The suggested model, which investigates the thermal behavior of bioconvection flow in CNTs/water based hybrid nanofluid, gyrotactic microbes, when considered alongside Soret-Dufour impact, contribute notably to important uses in biotechnology, energy systems, and industrial heat management. It is important to optimize bioreactors that require high microbial activity and heat transfer, to improve the design of innovative cooling systems that use hybrid nanofluid, and to aid in the creation of efficient microfluidic devices. Artificial neural networks provide accurate prediction of complex fluid-microbe interactions, which aids in the design and control of next-generation thermal and bioengineering processes. From reference results, execute LMS-FNN validation, training, and testing to get approximated solutions for variations connected with the physical system and to demonstrate the correctness of the suggested LMS-FNN. The Mean squared error, histograms, and regression analysis are used to examine the performance of LMS-FNN, and the problem is satisfactorily solved. The microorganism profile profile declines as the Peclet number increases.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04495-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of glycine conjugated polypropyleneimine dendrimers for targeted chloroquine delivery","authors":"Gaurav Kant Saraogi,&nbsp;Ashish Kumar Parashar,&nbsp;Haya Khader Ahmad Yasin,&nbsp;Jyoti Ahirwar,&nbsp;Mamta Kumari","doi":"10.1186/s11671-026-04570-1","DOIUrl":"10.1186/s11671-026-04570-1","url":null,"abstract":"<div><p>This study investigates the synthesis and evaluation of glycine-conjugated 5.0G polypropyleneimine dendrimers as an advanced nanocarrier system for the selective delivery of chloroquine. 5.0G PPI dendrimers were synthesized via a divergent method and subsequently conjugated with glycine. Comprehensive characterization confirmed the successful modification and an increase in nanocarrier size. Drug loading studies demonstrated a significantly enhanced entrapment of chloroquine (57.5% vs. 44.5%) in glycine-conjugated formulations, attributed to the potential complexation and sealing of dendritic branches. In vitro release profiles revealed a substantial reduction in chloroquine release from the glycine-coated dendrimers, indicating a sustained-release capability crucial for prolonged therapeutic action (only 32.07% release over 24 h compared to 88.80% from uncoated). Crucially, macrophage uptake studies indicated a four-fold reduction in phagocytic uptake of glycine-conjugated formulations, suggesting an effective bypass of macrophage recognition, thereby potentially minimizing non-specific clearance. In vivo pharmacokinetic analyses in rats showed a prolonged plasma concentration of chloroquine with the glycine-conjugated system, extending detectability up to 11 h. Furthermore, organ distribution studies highlighted a remarkable increase in liver accumulation (45.6% of the initial dose) compared to uncoated dendrimers or free drug, indicating highly effective liver targeting. These findings highlight the significant potential of glycine-conjugated PPI dendrimers to achieve highly efficient and sustained targeted drug delivery, particularly to the liver, while simultaneously reducing macrophage-mediated clearance.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04570-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-fidelity machine learning models for predicting antibacterial effects of cerium oxide nanoparticles across bacterial strains","authors":"Omar Almomani,&nbsp;Magdi E. A. Zaki,&nbsp;Raed Alfilh,&nbsp;Gadug Sudhamsu,&nbsp;Prabhat Kumar Sahu,&nbsp;Murari Devakannan Kamalesh,&nbsp;Sumit Sharma,&nbsp;Sobhi M. Gomha,&nbsp;Samim Sherzod","doi":"10.1186/s11671-026-04604-8","DOIUrl":"10.1186/s11671-026-04604-8","url":null,"abstract":"<div><p>The primary objective of this study is to develop and validate robust data-driven models for accurately predicting bacterial growth inhibition induced by cerium oxide nanoparticles across different bacterial strains and experimental conditions. This study aims to develop and validate data-driven predictive models to quantify bacterial growth inhibition induced by cerium oxide nanoparticles under diverse experimental conditions, with the goal of supporting antibacterial nanotechnology research. To this end, sophisticated AI methods, including Convolutional Neural Networks (CNN), Multi-layer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Ensemble Learning (EL), were employed to model bacterial cell concentration (OD600) with high precision. Model hyperparameters were optimized using the Coupled Simulated Annealing (CSA) technique to enhance predictive performance. A comprehensive dataset comprising 484 experimental observations was compiled, with 387 samples allocated for training and 97 for validation. The study considers two bacterial strains, <i>Escherichia coli</i> and <i>Bacillus subtilis</i>, cultivated in media containing cerium oxide nanoparticles with nominal sizes of 6 ± 3.5 nm, 15 ± 4.3 nm, 22 ± 5.7 nm, and 40 ± 10 nm (Samples A–D). Input features included bacterial type, nanoparticle size (medium type), nanoparticle concentration, and exposure time. Monte Carlo sensitivity analysis revealed that exposure time is the dominant factor governing bacterial cell concentration, followed by nanoparticle concentration, nanoparticle size, and bacterial strain. Among the evaluated models, MLP-ANN exhibited the highest predictive accuracy, achieving the greatest R² values and the lowest RMSE and AARE%. Beyond predictive performance, the results provide insight into key drivers of nanoparticle-induced antibacterial activity and demonstrate how data-driven modeling can guide experimental prioritization. Overall, the proposed framework serves as a complementary tool to laboratory experiments, supporting more efficient investigation of antibacterial effects while preserving the necessity of experimental validation. These results demonstrate that AI-based models, particularly MLP-ANN, serve as a powerful complementary tool to laboratory experiments by enabling accurate prediction, guiding experimental prioritization, and reducing experimental burden while maintaining the necessity of experimental validation.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04604-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning examination based on Bayesian regularized algorithm for slip effects on solarized Boger nanofluid with activation energy","authors":"Saba Liaqat,&nbsp;Abdulbasit A. Darem,&nbsp;Abed Saif Ahmed Alghawli,&nbsp;Munawar Abbas,&nbsp;Durdana Rustamova Farkhad,&nbsp;Ayele Tulu","doi":"10.1186/s11671-026-04555-0","DOIUrl":"10.1186/s11671-026-04555-0","url":null,"abstract":"<div><p>This model is highly helpful in thermal management and complex energy systems, particularly where precise control over heat and mass transfer is required. Solarized nanofluids can be used to improve heat absorption and transfer in solar thermal collectors, photovoltaic cooling systems, and energy storage devices. The inclusion of slip effects, thermophoresis, and Brownian motion makes the model applicable to micro- and nano-scale devices such as MEMS, micro reactors, and biomedical cooling systems. Furthermore, Bayesian regularization-based machine learning analysis enhances prediction accuracy for complex nonlinear behaviors, making the model effective in industrial process optimization, polymer processing, and chemical reactors that use non-Newtonian fluids with activation energy effects. The heat generation influence on magnetohydrodynamic (MHD) solarized Boger nanofluid under slip velocity and activation energy effects are examined in this paper. The nonlinear relationship between the governing physical parameters and the resulting flow and heat transfer behaviour is modelled using an artificial intelligence-based neural network framework that has been optimized using the Intelligent Bayesian Regularization technique. The neural network is trained using 80% of the generated dataset, with the remaining 20% being utilized for testing to ensure the suggested model is correct, dependable, and predictive. As the values of the chemical reaction parameter grow, the concentration profile decreases.</p></div>","PeriodicalId":51136,"journal":{"name":"Nanoscale Research Letters","volume":"21 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s11671-026-04555-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}