Next Materials最新文献

{"title":"The ZnO/Bentonite composite for sustainable tetracycline removal from water: Adsorption and photocatalysis for effective wastewater treatment","authors":"Yasmeen Hamdan ,&nbsp;Ahed H. Zyoud ,&nbsp;Samar Al-Shakhshir ,&nbsp;Shaher Zyoud ,&nbsp;Ameed Amireh ,&nbsp;Samer H. Zyoud ,&nbsp;Tae Woo Kim","doi":"10.1016/j.nxmate.2026.101655","DOIUrl":"10.1016/j.nxmate.2026.101655","url":null,"abstract":"<div><div>Pharmaceutical pollutants such as tetracycline (TC) pose serious environmental concerns because of their persistence and potential to promote antibiotic resistance. In this study, a ZnO/bentonite composite was developed and applied for the efficient removal of TC from aqueous solutions under simulated solar irradiation via a synergistic adsorption–photocatalysis mechanism. The bentonite component enhanced adsorption by drawing TC molecules closer to the ZnO active sites, where the reactive oxygen species (ROS) generated by ZnO facilitated degradation. SEM, XRD, FTIR, and UV–Vis characterization confirmed the composite’s structure and optical properties. Under optimal conditions (pH 8.5, 25 °C, 0.1 g/100 mL catalyst, 40 ppm TC), the system achieved 87 % removal, with complete mineralization validated by HPLC, total organic carbon (TOC), and Fourier transform infrared (FTIR) spectroscopy. The degradation followed pseudo-first-order kinetics with a rate constant of 0.015 min⁻¹ , and the point of zero charge (pHpzc = 9.4) influenced the pH-dependent performance. The catalyst retained 77 % of its initial efficiency after five cycles, highlighting its low cost, reusability, and eco-friendly potential for antibiotic-contaminated wastewater treatment.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101655"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026313","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":"Machine learning-based residual stress prediction in shot-peened components: Application to additively manufactured surfaces with stochastic roughness","authors":"Farbod Nazemi ,&nbsp;Mohammad Chamani ,&nbsp;Gholam Hossein Farrahi","doi":"10.1016/j.nxmate.2026.101667","DOIUrl":"10.1016/j.nxmate.2026.101667","url":null,"abstract":"<div><div>Shot peening is an efficient technique to improve the fatigue behaviour of metal components. In this study a hybrid approach, combining numerical and experimental is presented to investigate the influence of initial surface roughness on the distribution of residual stress in shot-peened components with particular attention to additively manufactured (AM) parts characterized by significant surface roughness (<span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>a</mi></mrow></msub><mo>=</mo><mn>10</mn><mo>−</mo><mn>35</mn><mspace></mspace><mi>µm</mi></mrow></math></span>). A 3D Finite Element (FE) model incorporating real rough surface morphology and strain-rate sensitive material model with nonlinear kinematic/isotropic hardening was developed, and subsequently was validated with experimental data available. A comprehensive methodology was employed, beginning with the development of a finite element model to simulate single and multi-random shot impacts on smooth and artificially generated AM-representative rough surfaces, while including stochastic surface topographies commonly seen in AM processes. Results indicate that SP decreased the roughness on surfaces with a high initial average roughness, R_a = 35.4 <em>μm</em>, by 16.67 %. However, the average surface roughness on samples that had lower roughness originally experienced an increase by 18 %. The results showed that the high roughness decreases the maximum and depth of the compressive residual stresses induced by SP, in comparison with smooth sample. Additionally, Machine Learning (ML) algorithms, Artificial Neural Networks and Random Forest, were applied to predict RS distribution based on SP parameters and initial surface roughness topography. The ML models were trained using a large dataset of all applicable process variables, which gives a significant predictive framework for SP applications.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101667"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173675","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":"Machine learning-driven optimization of biochar-based supercapacitors for sustainable energy storage: Mechanisms, trends, and perspectives","authors":"Fatima Sajjad ,&nbsp;Rashid Iftikhar ,&nbsp;Muhammad Ali Inam ,&nbsp;Saifullah Zadran ,&nbsp;Salman Haider ,&nbsp;Sahar Saleem ,&nbsp;Humayun Nadeem ,&nbsp;Alim-un-Nisa","doi":"10.1016/j.nxmate.2026.101971","DOIUrl":"10.1016/j.nxmate.2026.101971","url":null,"abstract":"<div><div>Global transition toward sustainable and efficient energy storage has intensified research into supercapacitors, valued for their rapid charge-discharge capability, high power density, and long operational lifespan. Among carbonaceous materials, biomass-derived biochar has emerged as a low-cost, renewable, and structurally tunable precursor for electrode design. This review provides a structured and critical synthesis of biochar-based supercapacitor strategies, systematically categorizing heteroatom doping, nanocomposite engineering, and hybrid architecture within an integrated electrochemical performance framework. Unlike existing literature that predominantly summarizes modification approaches, this review emphasizes cross-study comparability, mechanistic attribution, and device-level relevance to clarify transferable design principles. A consolidated analysis of recent machine learning (ML) applications reveals a paradigm shift from empirical optimization to data-driven predictive design. Benchmarking against reported datasets, descriptors, and model architectures, we confirm that tree-based ensemble and neural-network models (e.g., XGBoost, LightGBM, ANN) consistently achieve high predictive accuracy (R² &gt; 0.9), while also identifying current limitations in data standardization and descriptor harmonization. The review critically evaluates how ML can move beyond correlation towards interpretable and transferable optimization under realistic device constraints.</div><div>Finally, a converging research roadmap is proposed, prioritizing standardized reporting, device-level benchmarking, and constraint-aware ML integration to accelerate scalable implementation. By integrating materials engineering, data-driven modeling, and practical deployment considerations, this review establishes a comprehensive framework for advancing sustainable biochar-based supercapacitors toward real-world energy storage applications.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101971"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147600116","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":"Effect of friction-stir processing on microstructure and mechanical properties of Cu-Cr-Zr-Y alloy","authors":"M. Tkachev,&nbsp;E. Tkachev,&nbsp;A. Bodyakova,&nbsp;S. Mironov,&nbsp;R. Kaibyshev","doi":"10.1016/j.nxmate.2026.101605","DOIUrl":"10.1016/j.nxmate.2026.101605","url":null,"abstract":"<div><div>This study examined the effect of friction stir processing (FSP) on the microstructure and properties of a novel Cu-Cr-Zr-Y alloy. In addition, the microstructural aspects of doping Cu-Cr-Zr bronze with yttrium were considered. It was found that owing to the extremely low solubility of yttrium in copper, this alloying element was primarily concentrated in a secondary phase. Owing to the low melting point of the Y-rich phase, it precipitated as coarse inclusions and thus exerted only a minor influence on the material behavior. In contrast, FSP was found to be quite feasible for the thermomechanical processing of the Cu-Cr-Zr-Y alloy. Specifically, a single FSP pass resulted in a more than three-fold increase in strength characteristics and simultaneously provided a substantial enhancement in electrical conductivity. This effect was attributed to the formation of an ultrafine-grained structure and the precipitation of Cr-rich dispersoids within the stir zone.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101605"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980664","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":"Silica derived from rice husk waste as anode material for lithium-ion battery: A comprehensive study","authors":"P.U. Nzereogu ,&nbsp;A.D. Omah ,&nbsp;F.I. Ezema ,&nbsp;E.I. Iwuoha ,&nbsp;A.C. Nwanya","doi":"10.1016/j.nxmate.2026.101615","DOIUrl":"10.1016/j.nxmate.2026.101615","url":null,"abstract":"<div><div>This research investigates the synthesis and characterization of silica extracted from rice husks sourced from Adani in Enugu State, Nigeria, and its application in lithium-ion batteries. In this work, two methodologies for extracting SiO₂ from rice husk were explored: rice husk pre-treatment process (acid leaching) and rice husk post-treatment process (alkaline digestion). The rice husks underwent acid leaching with 10 % HCl, followed by calcination at temperatures ranging from 500°C to 800°C. The sample post-treatment involved alkaline digestion using a 1 N sodium hydroxide solution. Results from structural analysis showed a progressive increase in purity and amorphous properties of silica up to a calcination temperature of 700°C. At 800°C, crystalline forms of silica, such as cristobalite and tridymite, were observed. The post-treated samples, especially those calcined at 700°C (PT700), exhibited comparative higher purity. Electrochemical studies were carried out using Cyclic Voltammetry (CV), Galvanostatic Charge/Discharge (GCD) and Electrochemical Impedance Spectroscopy (EIS), and the results show that the post treated rice husk that was calcined at 700 °C (PT700) had the highest specific capacity of 913 mAh g⁻¹ at a current density of 100 mA g⁻¹ and retained ∼89 % of this capacity after 1000 charge-discharge cycles. The energy density of the PT700 sample was 302 Wh kg⁻¹ , demonstrating its potential as a viable alternative to graphite in lithium-ion battery applications. The study concludes that silica extracted from rice husks is a sustainable and efficient material for use in energy storage devices, offering significant advantages in terms of reactivity, surface area, and electrochemical performance.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101615"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980665","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":"Sustainable high capacitance supercapacitor electrode from biomass-derived iron and copper-doped graphene structures","authors":"Atti Srinivas,&nbsp;Sarat Chandra Babu Jakka","doi":"10.1016/j.nxmate.2026.101603","DOIUrl":"10.1016/j.nxmate.2026.101603","url":null,"abstract":"<div><div>In this study, BCG (Biochar-based graphene) was functionalized with iron (Fe) and copper (Cu) to enhance its capacitive behaviour and physicochemical properties. The structural, morphological, and surface characteristics were systematically analyzed using XRD, Raman spectroscopy, XPS, SEM, and BET analysis. Electrochemical analysis showed that BCG-Fe (iron-doped biochar-based graphene) achieved a specific capacitance of 187.72 F/g, with an energy density of 6.52 Wh/kg and power density of 2608 W/kg. BCG-Cu (copper-doped biochar-based graphene) exhibited 157.14 F/g, 5.45 Wh/kg, and 1510 W/kg, respectively. The enhanced performance of BCG-Fe is attributed to improved conductivity and redox activity from Fe doping. These results highlight the promise of metal-modified BCG as a scalable, eco-friendly electrode material for energy storage.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101603"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980668","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":"Additive manufacturing of Ti-Ni based ternary shape memory alloys","authors":"Yitao Chen,&nbsp;Frank Liou","doi":"10.1016/j.nxmate.2026.101614","DOIUrl":"10.1016/j.nxmate.2026.101614","url":null,"abstract":"<div><div>Metal additive manufacturing has become a powerful tool to develop customized metal alloys and to discover more advanced properties for novel extended applications. Ti-Ni based shape memory alloy is a group of intriguing smart functional materials, and adding a small amount of a third element can promote and induce more attractive functions. Due to the difficulty in traditional processing and the unique feature of material flexibility of in-situ alloying in additive manufacturing processes, not only Ti-Ni binary shape memory alloys but also Ti-Ni-X ternary shape memory alloys can be developed, manufactured, and investigated in-depth by additive manufacturing. This paper provides a brief review of the development of Ti-Ni based ternary shape memory alloys using metal additive manufacturing. The research status regarding a variety of Ti-Ni-X ternary alloys was summarized based on the classification of the two most widely used metal additive manufacturing processes: directed energy deposition and powder bed fusion. The main manufacturing issues were discussed and suggested, and the recommended research directions were made for future development.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101614"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026314","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":"Parametric optimization and performance prediction of stainless-steel ER308L cladding on mild steel via gas metal arc welding using RSM","authors":"Shiv Kumar ,&nbsp;Shahnwaz Alam ,&nbsp;Gagan Bansal","doi":"10.1016/j.nxmate.2026.101670","DOIUrl":"10.1016/j.nxmate.2026.101670","url":null,"abstract":"<div><div>The study is centered around the optimization of parameters and performance prediction for ER308L stainless steel welding on mild steel substrates using the Gas Metal Arc Welding (GMAW) process. The systematic alterations of the process variables (welding current, arc voltage, and travel speed) accompanied by Response Surface Methodology (RSM) grounded on Central Composite Design (CCD) aim at enhancing not only the surface characteristics but also the strength of the bond. The effect of factors on the critical reactions was determined by clad hardness, tensile strength, and dilution using statistical modeling and analysis of variance (ANOVA). The experimental validation confirmed that the regression models in question were extremely precise, as indicated by the correlation coefficients (R² &gt; 0.97) between predicted and experimental values. The response surface and desirability plots revealed that an ideal heat input condition (moderate current and voltage with controlled travel speed) has led to an enhancement in clad properties—maximum hardness of approximately 360 HV, tensile strength of more than 440 MPa, and dilution of less than 10 % being the lowest. Microstructural examination confirmed the presence of a strong metallurgical bond at the interface which was marked by the presence of small and compact dendritic structures in the clad area and resistance tests showed that the substrate had a better resistance. The optimized sample was found to have a very good combination of mechanical properties and surface quality, thus the use of RSM as a prediction and optimization tool for GMAW cladding was confirmed. This study sets the groundwork for the design of processes and performance prediction in the field of surface engineering applications involving the use of dissimilar metal cladding.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101670"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173617","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":"Impact of precursors on nitrogen doped graphene: A review","authors":"S.N. Anyanwu ,&nbsp;M. Tamboura ,&nbsp;E.A. Gosteva ,&nbsp;M.V. Chichkov ,&nbsp;A.S. Bykov","doi":"10.1016/j.nxmate.2026.101719","DOIUrl":"10.1016/j.nxmate.2026.101719","url":null,"abstract":"<div><div>Since the discovery of graphene and its potential applications, extensive research has been conducted to improve its performance and expand its utility. Among the strategies explored, defect engineering—specifically the controlled introduction of heteroatoms like nitrogen—has emerged as a crucial approach to customizing graphene's properties. This review examines the role of selected precursor materials in synthesizing nitrogen-doped graphene (NDG), focusing on key factors such as nitrogen configurations (pyridinic, pyrrolic, and graphitic), nitrogen content in the final product, catalytic activity, and surface conductivity. The advantages and limitations of different precursor are analyzed, along with a critical evaluation of synthesis methods, which significantly influence these parameters. By exploring these aspects, this study aims to provide insights for optimizing nitrogen doping processes to advance graphene-based technologies.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101719"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173622","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":"Design and machine learning driven optimization of tunable periodic cross-diamond terahertz metamaterial absorber","authors":"Pujita Bhatt ,&nbsp;Prince Jain ,&nbsp;Anand Joshi","doi":"10.1016/j.nxmate.2026.101673","DOIUrl":"10.1016/j.nxmate.2026.101673","url":null,"abstract":"<div><div>A tunable absorber is proposed using a periodic cross-diamond (PCD) resonator array integrated with vanadium dioxide (VO₂) for terahertz applications. Electromagnetic simulations shows absorption peaks at 2.83 THz and 8.28 THz, with a full-width at half-maximum of 7.43 THz, corresponding to a relative absorption bandwidth of 126.7%. The absorption mechanism is analyzed through magnetic and electric field distributions along with parametric analyses are conducted to assess the effect on the absorber’s performance. To improve design optimization and reduce computational cost, regression-based machine learning (ML) models K-Nearest Neighbors, XGBoost, and Random Forest are employed to predict absorptivity across intermediate frequencies. The KNN model achieves excellent performance with an R² of 0.9997 and an MAE of 0.0002, reducing the required CST simulations by nearly 50 % and significantly accelerating the EM design process for terahertz applications.</div></div>","PeriodicalId":100958,"journal":{"name":"Next Materials","volume":"11 ","pages":"Article 101673"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079118","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}