jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haiyang Li | Kangping Cui | Wenming Wu
Source: Water (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Recovery of Effective Acid from Waste Generated in the Anodic Oxidation Polishing Process The high treatment costs associated with wastewater and waste solutions produced by the anodic oxidation polishing section significantly limit industry development. To address this challenge, the present study investigates the characteristics of polishing wastewater and waste solutions, employing extraction and ion exchange combined with diffusion dialysis to recover effective acids. For waste tank solutions, single and dual solvent extraction experiments were conducted to determine the optimal extraction system. Electrostatic potential and interaction region indicator (IRI) analyses were performed to provide theoretical justification. Regarding cleaning wastewater, resin adsorption was applied to selectively remove aluminium ions from waste acid solutions, facilitating effective acid recovery. Static and dynamic adsorption–desorption experiments were initially performed to identify suitable resins. Subsequently, optimised parameters—including adsorption and desorption concentrations, volumes, and flow rates—were systematically established through conditional experiments, and diffusion dialysis was applied to recover acids from the desorbed solutions. The experimental results indicate that tributyl phosphate (TBP) emerged as the optimal single extractant, achieving an effective acid extraction rate of 88.67% under a solvent ratio of 4:1 at a room temperature of 28 °C. A binary solvent system, composed of TBP with 20% sulfonated kerosene, demonstrated superior engineering feasibility due to its reduced viscosity and satisfactory extraction rate of 82.19%. Moreover, adsorption–desorption tests confirmed that the resin-based method effectively recovered acids from cleaning wastewater. Specifically, under optimal operational conditions—downstream adsorption at 0.3–0.5 bed volumes (BV) and 1.0 BV/h, coupled with counter-current desorption at 2 BV and 2.4 BV/h—the acid recovery rate reached ≥95% while removing ≥90% of aluminium ions. Additionally, employing 20% sulfuric acid solution for desorption in diffusion dialysis enabled cyclic desorption. Consequently, this study successfully achieved acid reuse and substantially lowered wastewater treatment costs, representing a promising advancement for anodic oxidation polishing processes. Polishing ; Anode ; Anodic oxidation ; Oxidation process ; Process (computing) ; Metallurgy ; Waste management ; Materials science ; Environmental science ; Chemistry ; Pulp and paper industry ; Electrode ; Computer science ; Engineering Anodic Oxide Films and Nanostructures ; Advanced Machining and Optimization Techniques ; Corrosion Behavior and Inhibition
Authors: Longlong Li | Maria Fyta
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Mechanical and electronic response of monolayer chromium trihalides CrX<sub>3</sub> (X = Cl, Br, I) under uniaxial strain Recent advances in two-dimensional (2D) magnetic materials have promoted significant progress in low-dimensional magnetism and its technological applications. Among them, atomically thin chromium trihalides (CrX<sub>3</sub> with X = Cl, Br, and I) are among the most studied 2D magnets due to their unique magnetic properties. In this work, we employ density functional theory calculations to investigate the mechanical and electronic properties of CrX<sub>3</sub> monolayers in the presence of in-plane uniaxial strain. We calculate the strain-dependent energetics, stress-strain relationships, electron localization functions, and electronic density of states for two distinct strain directions: zigzag and armchair. Our results show that the ferromagnetic phase remains the magnetic ground state over a wide range of strain in both zigzag and armchair directions. The mechanical response exhibits a distinct anisotropy between these two directions, as reflected by the maximum value in the stress-strain relationship. As the halogen atom X becomes heavier, the maximum stress decreases from 6.04 (6.40) GPa to 3.20 (3.33) GPa in the zigzag (armchair) direction. By analyzing the electron localization function, we show the strain-induced variations in bonding characteristics and their impact on the mechanical strength of the materials. We also observe a strain-induced energy splitting of the Cr-d and X-p orbitals in the electronic density of states, and a strain-induced band-gap transition (from indirect to direct) in the electronic band structure, leading to a reduction of the electronic band gap with a directional dependence on the applied strain. Our results provide fundamental insights into the effects of uniaxial strain on the mechanical and electronic properties of CrX<sub>3</sub> monolayers, which are relevant for their potential use in strain-engineered electronics and spintronics. Zigzag ; Magnetism ; Density functional theory ; Electronic structure ; Ferromagnetism ; Monolayer ; Band gap ; Anisotropy ; Electronic band structure MXene and MAX Phase Materials ; Inorganic Chemistry and Materials ; 2D Materials and Applications
Authors: Ruichen Li | Yuzhi Liu | Jiang Du | Yixiao Chen | Xuelan Wen | Wenrui Li | Di He | Liwei Wang | Ji Chen | Weiluo Ren
Source: ArXiv.org (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

Spin-Adapted Neural Network Wavefunctions in Real Space Spin plays a fundamental role in understanding electronic structure, yet many real-space wavefunction methods fail to adequately consider it. We introduce the Spin-Adapted Antisymmetrization Method (SAAM), a general procedure that enforces exact total spin symmetry for antisymmetric many-electron wavefunctions in real space. In the context of neural network-based quantum Monte Carlo (NNQMC), SAAM leverages the expressiveness of deep neural networks to capture electron correlation while enforcing exact spin adaptation via group representation theory. This framework provides a principled route to embed physical priors into otherwise black-box neural network wavefunctions, yielding a compact representation of correlated system with neural network orbitals. Compared with existing treatments of spin in NNQMC, SAAM is more accurate and efficient, achieving exact spin purity without any additional tunable hyperparameters. To demonstrate its effectiveness, we apply SAAM to study the spin ladder of iron-sulfur clusters, a long-standing challenge for many-body methods due to their dense spectrum of nearly degenerate spin states. Our results reveal accurate resolution of low-lying spin states and spin gaps in [Fe$_2$S$_2$] and [Fe$_4$S$_4$] clusters, offering new insights into their electronic structures. In sum, these findings establish SAAM as a robust, hyperparameter-free standard for spin-adapted NNQMC, particularly for strongly correlated systems. Wave function ; Antisymmetric relation ; Spin (aerodynamics) ; Context (archaeology) ; Artificial neural network ; Representation (politics) ; Symmetry (geometry) Machine Learning in Materials Science ; Quantum many-body systems ; Magnetism in coordination complexes
Authors: Xuan-Zhang Wang | Jianan Chi | Xiao Zhang | Guangshuai Lu | Xuan Li | Chunli Wang | Lijun Wang | Nannan Zhang
Source: Frontiers in Plant Science (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Early detection and severity classification of verticillium wilt in cotton stems using Raman spectroscopy and machine learning The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating disease. Traditional diagnostic methods, which often rely on manual expertise or destructive sampling, are limited by low efficiency and high subjectivity. In recent years, Raman spectroscopy has emerged as a promising solution due to its rapid, non-destructive, and highly sensitive characteristics for plant disease detection. In this study, we analyzed cotton stems using Raman spectroscopy, applying Savitzky-Golay (SG) smoothing combined with multiple preprocessing methods including Scaling and Shifting (SS), Standard Normal Variate (SNV), inverse first-order differential (1/SG)', and multiplicative scatter correction (MSC). For baseline correction, we employed polynomial fitting (PolyFit) and adaptive iterative weighted penalized least squares (airPLS). Feature selection was performed using principal component analysis (PCA), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS).Three optimized models were developed: support vector machine (SVM) with weighted mean of vectors (INFO) algorithm, random forest (RF) enhanced by particle swarm optimization (PSO), and long short-term memory (LSTM) network optimized via chameleon swarm algorithm (CSA).The results show that the INFO-SVM model with SG-airPLS-(1/SG)' -CARS preprocessing demonstrated superior performance, achieving 97.5% accuracy (0.974 F1-score) on training data and 90.0% accuracy (0.867 F1-score) on validation data, outperforming both PSO-RF and CSA-LSTM models. These results confirm that Raman spectroscopy integrated with optimized machine learning enables accurate VW classification in cotton stems. This method enables early disease detection during infection, facilitating timely fungicide application and reducing yield losses. Support vector machine ; Preprocessor ; Pattern recognition (psychology) ; Feature selection ; Random forest ; Verticillium wilt ; Principal component analysis ; Fusarium wilt Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Wen‐Bin Yu | Chunjie Xu | Nan Li | Xiangquan Wu | Shang Sui
Source: Corrosion Reviews (article, 2025-05-28)
Field: Materials Science — Biomaterials

Effect of Al and Y addition on corrosion properties of magnesium alloy Abstract Due to its lightweight structure, magnesium alloys have been widely used in the automotive and aerospace industries. However, the corrosion resistance of magnesium alloy is a key factor limiting their further application. Rare earth alloying is an economical and effective method to improve the corrosion resistance of magnesium alloys. In this work, Mg–2Y–2Al (wt%) alloy was prepared by conventional casting method. After 168 h of immersion in 3.5 wt% NaCl solution, the corrosion rates determined by hydrogen evolution test and mass loss experiment were 0.57 mm/y and 1.01 mm/y, respectively. The results provide a basic understanding of the synergistic effect of Al and Y on enhancing the corrosion resistance of magnesium alloys. The co-addition of Al and Y is conducive to the formation of a stronger protective film on the alloy surface and effectively inhibits the corrosion of alloys. It provides important insights for the future development of high-performance and corrosion-resistant magnesium alloys. Materials science ; Corrosion ; Magnesium ; Alloy ; Metallurgy ; Magnesium alloy ; Materials processing Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloys Composites Properties
Authors: Mingqian Li | Lifeng Wang | Zhuoqun Zheng
Source: npj Computational Materials (article, 2025-12-14)
Field: Materials Science — Materials Chemistry

Coarse-grained machine learning potential for mesoscale multilayered graphene A coarse-grained neuroevolution potential (CGNEP) for multilayered graphene based on an ab initio accuracy dataset is developed for mesoscale molecular dynamics simulations. The information loss in coarsening process is discussed and divided into intralayer part and interlayer part. The CGNEP describes the interlayer shear introduced by van der Waals interactions well by modifying the descriptor of NEP. The mechanical properties and vibration frequencies of structures of different sizes are well predicted via CGNEP. Compared with the traditional empirical CG potential, the CGNEP possesses interlayer properties of the structure of graphene and maintains the ability for higher mapping ratio coarsening. The frequencies of a 12-layer graphene membrane with a length and width of 1 μm are directly calculated via the CGNEP with a 64:1 mapping ratio and compared with the experimental results. The proposed CGNEP may be further used for other multilayered CG 2D materials. Graphene ; Mesoscale meteorology ; van der Waals force ; Ab initio ; Process (computing) ; Vibration ; Shear (geology) Graphene research and applications ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: Sampreeti Bhattacharya | Arkaprava Roy
Source: ChemRxiv (preprint, 2025-01-21)
Field: Materials Science — Materials Chemistry

A Look Into Solubility Variations Via a Novel Nonparametric Additive Characterization Learning aqueous solubility remains a key challenge in drug development for improving oral bioavailability. Traditional data-driven solubility estimations using standard supervised models, however, can often suppress the information embedded in a molecule’s chemical properties and the intricate connectivity of its atoms. In addition to the predictive analyses for solubility, it is also crucial to investigate the underlying scientific factors that influence the aqueous solubility of potential drug candidates. This paper attempts to learn such properties and further proposes a novel nonparametric model with an additive-type characterization that provides insights into the differential contribution of the chemical and graphical descriptors, that are important in learning a molecule’s solubility in an aqueous solvent. The dataset used in this work includes over 9000 experimental structural, chemical, and electronic properties. Using graph-theoretic attributes derived from molecular graphs, we characterize the structural traits of the molecules. Our analysis reveals several intriguing properties of solubility and its physicochemical relationship with both structural and chemical features of the molecules, specifically based on their weights and shapes. Nonparametric statistics ; Characterization (materials science) ; Solubility ; Mathematics ; Chemistry ; Biological system ; Econometrics ; Materials science ; Nanotechnology ; Organic chemistry ; Biology Crystallization and Solubility Studies
Authors: Guido Deissmann | Erika Neeft | Diederik Jacques
Source: Frontiers in Nuclear Engineering (article, 2025-01-20)
Field: Chemistry — Inorganic Chemistry

EURAD State-of-the-Art Report: Assessment of the chemical evolution at the disposal cell scale – part II – gaining insights into the geochemical evolution Long time frames are to be considered in the safety and performance assessment of deep geological disposal of intermediate and high level radioactive waste. Geochemical conditions will change in the waste, conditioning matrix, waste package, engineered barriers and the host rock–all components present at the disposal cell scale. This aspect of geological disposal was the focus of the work package ACED (Assessment of chemical evolution of intermediate level (ILW) and high level (HLW) waste at disposal cell scale) in the EURAD project (the European Joint Programme on Radioactive Waste Management). The first part of this review provided a narrative of the geochemical evolution of the disposal cell. In this second part, an overview is given about methods and approaches that can be used to gain further insights into the processes driving the geochemical evolution, more in particular (i) laboratory and in-situ experiments, (ii) archaeological and natural analogues, and (iii) modelling tools. The review concludes with a short discussion on the consequences on material properties, waste forms and radionuclide mobility. Scale (ratio) ; State (computer science) ; Earth science ; Geology ; Computer science ; Geography ; Cartography ; Programming language Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Groundwater flow and contamination studies
Authors: Zahra Ghavi | Abdolhalim Torrik | Mozafar Rezaee | Mahdi Zarif
Source: Scientific Reports (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Molecular dynamics insights into non-covalent functionalization of boron nitride nanotubes for doxorubicin delivery Developing effective drug delivery systems is an ongoing challenge for the treatment of cancer, in particular, enhancing the efficacy of chemotherapeutic agents like doxorubicin. This study explores the potential of pristine and non-covalently functionalized boron nitride nanotubes (BNNTs) as carriers for doxorubicin, leveraging their chemical stability, biocompatibility, and mechanical strength. Molecular dynamics simulations were conducted to analyze the effect of adding functional groups such as chitosan, ethylene glycol, poly-L-lysine, and folic acid onto the surface of BNNTs. The impact of polymers, their chain length, and the number of functional groups on optimizing drug loading profiles was investigated. Functionalization of boron nitride has increased hydrogen bonding with water and drugs. As a result, boron nitride functionalization increases the solubility of BNNTs and enhances drug loading. Analysis of the radial distribution function shows that the non-covalent functionalization of boron nitride causes the drug to be more adsorbed on the nanotube wall. In contrast, the drug in the pristine nanotube is more trapped inside it. From van der Waals (vdW) interaction energies and free energy calculations, it was revealed that functionalization using poly-L-lysine enhanced the interactions between the carrier and doxorubicin. These findings underscore the potential of functionalized BNNTs as effective carriers for targeted cancer therapy. Boron nitride ; Surface modification ; Covalent bond ; Doxorubicin ; Posttranslational modification ; Boron ; Molecular dynamics ; Nanotechnology ; Materials science ; Computer science ; Chemistry ; Medicine ; Biochemistry ; Enzyme ; Computational chemistry ; Internal medicine ; Organic chemistry ; Chemotherapy Boron and Carbon Nanomaterials Research ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: Sayan Ganguly | Shlomo Margel
Source: Molecules (review, 2025-08-22)
Field: Materials Science — Biomaterials

Magnetic Nanoparticle-Based Nano-Packaging and Nano-Freezing in Food Storage Applications Magnetic nanoparticles (MNPs) have emerged as essential agents in food preservation, tackling significant issues related to shelf life extension, quality maintenance, and safety assurance. This thorough analysis consolidates current developments in MNP-based nano-packaging and nano-freezing technologies, emphasizing their processes, effectiveness, and commercial feasibility. Metallic nanoparticles augment packaging efficacy via antibacterial properties, oxygen absorption, and real-time freshness assessment, while transforming freezing techniques by inhibiting ice crystal development and maintaining cellular integrity. Notwithstanding their potential applications, regulatory uncertainties, toxicity issues, and scalability challenges necessitate collaborative multidisciplinary approaches. We rigorously survey the technological, environmental, and safety aspects of MNP deployment in the food sector and suggest research priorities for sustainable implementation. Nano- ; Nanoparticle ; Nanotechnology ; Food packaging ; Materials science ; Chemistry ; Food science ; Composite material Nanocomposite Films for Food Packaging
Authors: Jakia Mustary Lina | M. Mostafa | Samia Yeasmin | Nishat Tasnim | Asif Rahman Dipto | Harinarayan Das | Md. Nurul Amin | N. M.-Mofiz Uddin Khan | Ahsan Habib | Mahmudur Rahman
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Green-synthesized silver nanoparticles for improved heat stress resilience and germination in potato seeds Climate change-induced heat stress severely impedes potato (<i>Solanum tuberosum</i>) germination, threatening global food security. Here, we report the application of green-synthesized silver nanoparticles (AgNPs), fabricated using <i>Azadirachta indica</i> (neem) leaf extract, as nanopriming agents to enhance germination and thermotolerance. The green-synthesized AgNPs exhibited a smaller crystallite size (9.7 nm) compared to chemically synthesized AgNPs (20.6 nm), with higher colloidal stability (zeta potential -55.2 mV <i>vs.</i> -35.7 mV). At the optimal priming concentration (5 mg L<sup>-1</sup>), green AgNPs increased germination on the 12th day by 19% relative to chemical AgNPs and by 50% over hydroprimed controls. Under elevated temperature (32.2 °C), green AgNP-primed seeds maintained a consistent 10% higher germination rate than controls and showed faster radicle emergence. ICP-MS confirmed greater nanoparticle uptake in primed seeds (144 ppm Ag for green AgNPs <i>vs.</i> 105 ppm for chemical AgNPs, compared to 1.98 ppm in hydroprimed seeds). Enhanced water uptake was also evident, with an 82% increase in seed mass after green AgNP priming compared to 44% in hydroprimed seeds. A preliminary techno-economic analysis confirmed the superior cost-effectiveness of the green synthesis route. Collectively, these findings establish green-synthesized AgNP nanopriming as a cost-effective, sustainable, and biologically superior strategy to improve potato germination and heat stress resilience, offering a promising avenue for climate-smart agriculture. Germination ; Silver nanoparticle ; Radicle ; Priming (agriculture) ; Nanoparticle ; Heat stress ; Degradation (telecommunications) Nanoparticles: synthesis and applications ; Carbon and Quantum Dots Applications
Authors: Leon Merker | Bojing Zhang | Jun Yuan | Shanling Ji | Helge S. Stein
Source: Batteries & Supercaps (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Insight Generation from Information‐Dense Formation Protocols Accelerated formation protocols that utilize pulsed charging offer an unprecedented wealth of electrochemical data. Herein, methods are presented to extract diagnostic data relating to pseudodiffusion coefficients, internal resistance, and others that give live insight into solid electrolyte interphase (SEI) growth. Specifically, a purely mathematical method is used to track formation progression in near‐real time and chart a path toward incorporation of adjustable pulse parameters for targeted SEI synthesis. The method and analysis are performed on 3 mAh cells but can also be applied to higher‐capacity cells. Computer science Machine Learning in Materials Science