jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicodemo Mazzaferro | Suemin Lee | Pilar Cossio | Pratyush Tiwary | Glen M. Hocky
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-07)
Field: Materials Science — Materials Chemistry

Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless <i>γ</i> metric from the Exponential Average Time-dependent Rate (EATR) method, which represents the fraction of a biasing potential along the RC that contributes to increasing the rate constant. We demonstrate that <i>γ</i> can be used to test whether the utility of a reaction coordinate for predicting kinetics with a Meta-dynamics bias improves as the coordinate is iteratively updated to include new data. We evaluate reaction coordinates approximated via the iterative State Predictive Information Bottleneck (SPIB) approach, which was previously shown to be accurate across six protein-ligand dissociation systems. For these same systems, we compute <i>γ</i> values and mean accelerated times . After systematically scanning over fitting parameters, the results show that <i>γ</i> increases closer to 1, while decreases, revealing a consistent inverse correlation. These results demonstrate that <i>γ</i> serves as a practical criterion for RC evaluation and offers guidance for selecting SPIB-derived coordinates yielding quantitative kinetic predictions. Biasing ; Kinetics ; Computer science ; Quality (philosophy) ; Econometrics ; Chemistry ; Physics ; Economics ; Engineering ; Voltage ; Electrical engineering ; Classical mechanics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Jianghai Xia | Yunong Tian | Xianwei Hao | Yuhan Peng | Guanqun Luo | Zhihua Gan
Source: Biotechnology for Biofuels and Bioproducts (article, 2025-08-11)
Field: Chemistry — Analytical Chemistry

A model for tobacco growing area classification based on time series features of thermogravimetric analysis Biomass is greatly influenced by geographic location, soil composition, environment, and climate, making the efficient and accurate identification of growing areas highly significant. This study proposes a classification model for tobacco growing areas based on time series features from thermogravimetric analysis (TGA). This study combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) model to process the derivative thermogravimetric (DTG) data, aiming to uncover the inherent time series properties and the continuous and dynamic relationship between temperatures for classifying tobacco growing areas. By analyzing 375 tobacco samples from ten different provinces, CNN is employed to extract local features, while LSTM captures long-term dependencies in the DTG data. The dataset used in this study has a limited sample size, a wide variety of classes, and an imbalance in the number of samples across these classes. Despite these challenges, the model achieves 86.4% accuracy on the test set, significantly surpassing the performance of the traditional Support Vector Machine model, which only achieves 68.2% accuracy. Additionally, the model reveals key temperature ranges crucial for growing area classification associated with the pyrolysis temperature ranges of volatile components, hemicellulose, cellulose, lignin, and CaCO<sub>3</sub> in the tobacco. This model lays the groundwork for the future use of geographical labels to accurately represent tobacco's style and quality, enabling more precise differentiation and improved quality control. Thermogravimetric analysis ; Hemicellulose ; Computer science ; Data mining ; Discriminative model ; Process (computing) ; Sample (material) ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Machine learning ; Cellulose ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Tushal Kyada
Source: International Journal of Metallurgy and Alloys (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Aluminum Alloy as Sacrificial Anode for Corrosion Protection: A Review Aluminum sacrificial anodes are widely used for cathodic protection in marine, offshore, and energy applications due to their light weight, high current efficiency, and favorable electrochemical potential.Pure aluminum, however, rapidly passivates in seawater, reducing protective performance.Alloying with elements, such as zinc, indium, gallium, tin, and bismuth, improves activation, ensures uniform dissolution, and stabilizes long-term behavior.This review examines the influence of alloying elements on electrochemical performance, microstructure, and mechanical properties, highlighting correlations between intermetallic formation, grain refinement, and anodic efficiency.Experimental studies, including electrochemical testing, weight-loss measurements, and field trials, demonstrate the advantages of optimized aluminum alloys over traditional zinc and magnesium anodes.Applications in ships, offshore platforms, pipelines, storage tanks, and renewable energy systems are discussed, emphasizing the importance of tailored alloy design for reliable and long-lasting protection.Finally, key research gaps are identified, underscoring opportunities for environmentally sustainable alloy development, advanced microstructural engineering, and extended field validation to enhance anode performance in diverse corrosive environments. Galvanic anode ; Corrosion ; Alloy ; Anode ; Aluminium ; Nickel alloy ; Weldability ; Pitting corrosion Corrosion Behavior and Inhibition ; Anodic Oxide Films and Nanostructures ; Molten salt chemistry and electrochemical processes
Authors: Chijioke B. Ugwuodo | Henry Ukochukwu Itiri | Ngozi Benedict Sunday | I. N Emmanuel | Kelechi N. Akatobi
Source: Journal Of Engineering Research Innovation And Scientific Development (article, 2025-05-10)
Field: Chemistry — Analytical Chemistry

Modelling and Optimization of Bioethanol Production from Cassava Waste Slurry Using Artificial Neural Network and Adaptive Neuro-fuzzy Inference Systems The increasing demand for alternative renewable energy sources has driven research into bioethanol production as a sustainable solution. This study investigates the production of bioethanol from cassava waste slurry using Saccharomyces cerevisiae. Process optimization and predictive modeling were conducted using Artificial Neural Networks (ANN) andAdaptive Neuro-Fuzzy Inference Systems (ANFIS). The research involved experimental fermentation, data collection, and computational modeling to analyze the effects of temperature, pH, and sugar concentration on ethanol yield. A total of 19 experimental runs were conducted, and the models were evaluated based on statistical indices, including mean square error (MSE) and coefficient ofdetermination (R²). The results demonstrated thatANFIS exhibited superior predictive accuracy, with an R² value of 0.9999 and a minimal error of 0.0000013607, outperforming ANN. The optimal conditions for bioethanol production were identified as 32.5 °C, 0.238 mol/dm³ sugar concentration, and pH 5.25, yielding 30% ethanol. Fourier Transform Infrared (FTIR) spectroscopy confirmed the presence ofethanol, validating the effectiveness ofthe fermentation process. The findings highlight the potential ofcassava waste as a viable feedstock for sustainable bioethanol production and emphasize the advantage ofsoft computing techniques in optimizing bioethanol yield. These results contribute valuable insights into biofuel research, paving the way for efficient, cost-effective, and environmentally friendly bioethanol production processes. Artificial neural network ; Adaptive neuro fuzzy inference system ; Production (economics) ; Neuro-fuzzy ; Slurry ; Biofuel ; Biochemical engineering ; Inference system ; Process engineering ; Computer science ; Environmental science ; Fuzzy logic ; Waste management ; Artificial intelligence ; Engineering ; Fuzzy control system ; Environmental engineering ; Economics Spectroscopy and Chemometric Analyses
Authors: Vivek Chintada | Narasimha Golla
Source: Material Science Research India (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Integrating Toxicology and Materials Science or Safer Metal-Based Products ABSTRACT: The surging demand for metal-based products in various industries has highlighted the critical need for ensuring their safety and minimizing environmental impact. This review article delves into how the collaboration between toxicology and materials science can be used as a strategic approach to improve the safety and sustainability of metal-based products. By amalgamating insights from toxicological evaluations and advancements in materials science, this interdisciplinary framework aims to revolutionize the development and production of metal goods. Through toxicology, the assessment of the detrimental effects of metal compounds on human strength and the environment is conducted, providing valuable data to inform materials science research and innovation. By leveraging this integrated approach, manufacturers can optimize material selection, manufacturing processes, and product design to create safer and more sustainable metal-based products that meet both performance requirements and safety standards. Continuous collaboration and knowledge sharing between toxicologists and materials scientists hold the key to driving ongoing innovation and advancing the development of next-generation metal products that prioritize human health and environmental well-being. KEYWORDS: Environmental impact; Human health; Innovation; Integration; Materials science; Metal-based products; Safety; Sustainability; Toxicology SAFER ; Toxicology ; Forensic engineering ; Engineering ; Environmental science ; Computer science ; Computer security ; Biology Nanoparticles: synthesis and applications ; Recycling and Waste Management Techniques ; Microplastics and Plastic Pollution
Authors: Baptiste Labonne | Christine Guéneau | Marjorie Bertolus
Source: Physical Review Materials (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Combined interatomic potential and electronic structure investigation of the cubic phases of the Am-O system The knowledge of the properties of americium oxides is crucial for nuclear fuel optimization and potential future use in radioisotope thermoelectric generators. In this paper, we used a combination of state-of-the-art complementary atomic scale modeling methods to determine the properties of the cubic phases of americium oxides. We used the Cooper-Rushton-Grimes (CRG) interatomic potential updated for the study of americium-bearing oxides to study the structural and thermodynamic properties of the cubic phases of americium oxides ${\mathrm{AmO}}_{x}$ with $1.5\ensuremath{\le}x\ensuremath{\le}2$, i.e., from the sesquioxide C-${\text{Am}}_{\text{2}}{\text{O}}_{3}$ to the dioxide ${\text{AmO}}_{\text{2}}$, between 250 K and 4500 K. A large number of properties were calculated as a function of composition and temperature: cell parameter, thermal expansion coefficient, enthalpy increment and specific heat capacity. The results reproduce almost exactly the melting temperature of the sesquioxide and show a phase transition at high temperature for the ${\mathrm{AmO}}_{1.55}$ and ${\mathrm{AmO}}_{1.60}$ compounds, which could result in a phase observed experimentally for these O/M ratios. They also allow us to recover the only melting temperature reported for ${\text{AmO}}_{\text{2}}$ in literature but for a much lower O/M ratio, approximately around 1.55--1.60. The reduction of ${\text{AmO}}_{\text{2}}$ to ${\text{AmO}}_{\text{2-x}}$ in temperature cannot, however, be obtained using the CRG empirical potential formalism due to the fixed charge formalism. We therefore performed electronic structure calculations to determine the properties of ${\text{AmO}}_{\text{2}}$ in temperature up to around 3000 K using density functional theory in the $\mathrm{DFT}+U$ approximation and using a functional taking into account van der Waals interactions. The enthalpy increment and specific heat capacity obtained are in excellent agreement with the available measurements and calphad modeling. The formation of oxygen dimers separated from the crystal and the reduction of ${\text{AmO}}_{\text{2}}$ is observed, with the variation in O/M ratio as a function of temperature agreeing with the results of calphad calculations. The results presented in this paper provide significant insight into the properties of the cubic phases of americium oxides and complement the little experimental data on this system, especially at high temperature, and are now available to refine phase diagram calculations and fuel performance codes simulations. Sesquioxide ; Interatomic potential ; Enthalpy ; Thermoelectric effect ; Heat capacity ; Electronic structure ; Density functional theory ; van der Waals force ; Thermal expansion Nuclear Materials and Properties ; Nuclear materials and radiation effects ; Rare-earth and actinide compounds
Authors: Kamel Benlouanas | Lazhar Yahia
Source: ASEAN Journal of Scientific and Technological Reports (article, 2025-09-21)
Field: Materials Science — Metals and Alloys

Experimental Investigation of Pitting Corrosion Behavior of 304L Stainless Steel on MnS Inclusions in Chloride Environments Applied to the Mediterranean Industry Structures made of stainless steel experience a gradual deterioration in their fundamental properties when exposed to both mechanical stress and harsh environmental conditions. This study aims to analyze the impact of cold tensile deformation on the localized roughness corrosion of 304L stainless steel in a 3% NaCl solution, mimicking seawater conditions. Corrosion tests were performed on samples obtained from standardised tensile specimens of the Public Economic Enterprise for the Production of Bolts, Cutlery, and Faucets (BCR) in Boumerdes, Algeria, which had experienced deformation at strain levels of 2.18%, 3.63%, 10.90%, and 16.36%. The results, including corrosion susceptibility, pitting behavior, and repassivation potentials, were evaluated and compared based on the strain. Findings indicate that all measured potentials decrease as the strain increases, except for the roughness potential, which shows a significant decline. This suggests a notable reduction in the material's corrosion resistance with higher deformation levels. Pitting corrosion ; Corrosion ; Ultimate tensile strength ; Deformation (meteorology) ; Chloride ; Surface roughness ; Surface finish ; Tensile testing Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Shuze Zhu
Source: Nanomaterials (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Nanomechanics of Multi-Walled Carbon Nanotubes Growth Coupled with Morphological Dynamics of Catalyst Particles Low-dimensional carbon nanostructures such as nanotubes, nanocones, and nanofibers can be grown in chemical vapor deposition (CVD) synthesis using catalyst nanoparticles. It is commonly observed that the morphology of solid catalyst nanoparticles continuously fluctuates during multi-walled carbon nanotube (MWCNT) growth. Interestingly, when the diameter of the inner tube of the growing MWCNT reduces below a threshold value, the catalyst nanoparticle snaps out of the MWCNT and recovers its spherical shape. If the MWCNT is tapered, the catalyst nanoparticle may also break. In this study, large-scale molecular dynamics simulations and nanomechanical modeling are employed to elucidate the complete process of MWCNT growth coupled with morphological change in the catalytic nanoparticles. It is shown that the tendency to decrease the surface energy of the catalyst nanoparticle is the major underlying driving force for the variation in morphology under the mechanical constraint of the growing MWCNT. Importantly, the predicted critical inner CNT radius at the onset of the shape recovery is in excellent agreement with experimental observations. The combination of molecular dynamics simulations and theoretical modeling offer an alternative perspective on co-evolution of catalyst nanoparticles and the growth of low-dimensional carbon nanostructures. Nanomechanics ; Carbon nanotube ; Catalysis ; Nanoparticle ; Molecular dynamics ; Chemical vapor deposition ; Carbon nanofiber ; Carbon fibers ; Carbon nanotube supported catalyst Carbon Nanotubes in Composites ; Graphene research and applications ; Nanopore and Nanochannel Transport Studies
Authors: Adélaïde Savoy | Vladimir Gorbachev | Charlotte N. Stindt | Peter Chen
Source: Chemistry - A European Journal (article, 2025-10-23)
Field: Chemistry — Physical and Theoretical Chemistry

London Dispersion versus Intramolecular Hydrogen Bond in Bis‐Pyridines: How Accurate Is DFT for Competing Noncovalent Interactions in the Condensed Phase? We report a systematic investigation of noncovalent interactions-particularly an intramolecular hydrogen bond and London Dispersion forces-in singly protonated bis-pyridines, studied across solution and crystalline states. Building on our previous gas-phase study, we combine variable-temperature <sup>1</sup>H NMR spectroscopy, single-crystal X-ray diffraction, and density functional theory (DFT) calculations. The measured <sup>1</sup>H NMR chemical shifts of the acidic proton serve as a solution-phase structural readout, which we correlate with an independent crystallographic metric. By systematically varying the linker (-CH<sub>2</sub>-, -O-, and -CH<sub>2</sub>CH<sub>2</sub>-) and the pendant substituents (H, methyl, tert-butyl), we examine how increasingly bulky "dispersion energy donors" affect both the intramolecular hydrogen bond and the accessible conformational states. In reference systems, where a single noncovalent interaction governs the geometry, even relatively simple computational models correctly reproduce the experimentally observed structures. However, for molecules featuring two competing noncovalent interactions, the tested, dispersion-corrected, DFT often fails to predict the relative energies of accessible conformers accurately, highlighting current limitations in predictive accuracy. We briefly discuss broader implications of currently achievable predictive accuracy for homogeneous catalysis. Intramolecular force ; Non-covalent interactions ; Hydrogen bond ; Density functional theory ; Conformational isomerism ; Dispersion (optics) ; Molecule ; Proton Crystallography and molecular interactions ; Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications
Authors: Esraa Saleh Alwan | Faiq F. Karam | Zeina M. Kadam
Source: Bulletin of the Chemical Society of Ethiopia (article, 2025-03-21)
Field: Chemistry — Organic Chemistry

Kinetics, isotherms and thermodynamics study of eosin yellow dye adsorption from aqueous solutions by MnO2@rGO nanocomposite This study explores the use of MnO2@rGO nanocomposite as an adsorbent for Eosin Y dye. It was found that equilibrium for Eosin Y adsorption on the MnO2@rGO nanocomposite surface is achieved in 120 min. The nanocomposite demonstrated a high dye adsorption rate due to its extensive surface area, which facilitates substantial dye uptake‏. To assess the adsorption kinetics of Eosin Y on the MnO2@rGO surface; the data suggested that the pseudo-second-order model fit the data better. The Langmuir, Freundlich, and Temkin equations are examples of linear forms of adsorption isotherms that were investigated in this study and ‎the substantial correlation coefficient for the Freundlich isotherm model suggests that the ‎adsorption of Eosin Y dye on the MnO2@rGO nanocomposite surface is multilayered. Furthermore, the effects of pH, ionic strength, and thermodynamic factors on maximum adsorption were examined. In addition, thermodynamic functions including entropy (∆S), enthalpy (∆H), and Gibbs free energy (∆G) were evaluated and the adsorption is physical. KEY WORDS: MnO2@rGO nanocomposite, Eosin yellow dye, Adsorption, Adsorption isothermal, Characterization, Thermodynamics, Kinetics Bull. Chem. Soc. Ethiop. 2025, 39(6), 1213-1226. DOI: https://dx.doi.org/10.4314/bcse.v39i6.15 Chemistry ; Kinetics ; Aqueous solution ; Nanocomposite ; Adsorption ; Chemical engineering ; Eosin ; Thermodynamics ; Physical chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Amirhossein Banihashemi | Baran Emamgholivand | Shahram Mahboubizadeh | Omid Ashkani
Source: International Journal of Pioneering Technology and Engineering (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Latest known achievements in titanium alloys used in bio applications: a general review The development of titanium alloys in various medical fields is currently of great interest to researchers, and in this regard, the manufacture of various parts such as hip joints, knee joints, surgical equipment, and dental implants is of great interest. This study is a review of the latest achievements in this field, and the results show that the development of new generation alloys containing elements such as niobium and molybdenum can lead to the manufacture of wear-resistant alloys with lower elastic modulus, which are used in dental implants. This article also reviews some applications of titanium quantum dots, and it is suggested that researchers in the future should work on the study and development of quantum dots and their use in the development of quantum alloys. Titanium alloy ; Niobium ; Titanium ; Molybdenum ; Work (physics) Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Magnesium Alloys: Properties and Applications
Authors: Yuhe Yang | Zengkai Wang | Pu Hu | Feiyi Zhang | Lei Liu
Source: Chinese Science Bulletin (Chinese Version) (article, 2025-10-01)
Field: Materials Science — Biomaterials

Progress in conductive strategies for silk fibroin in smart wearables 蚕丝蛋白基智能可穿戴系统作为人工智能与精准医疗领域的前沿研究方向，其核心效能源于蚕丝蛋白兼具优异力学特性、界面相容性和结构可调控性等优势。本文系统综述了蚕丝蛋白导电改性材料及其在智能可穿戴领域的研究进展，阐述其作为智能可穿戴设备的分子结构基础，重点分析了五种导电功能化改性策略，比较不同改性方案的优劣性质与适用领域，并归纳其在生物电信号传感、能量转换收集及柔性储能等领域的研究成果，指出蚕丝蛋白基智能可穿戴设备在目前应用中仍需克服的问题和未来可能的发展方向，为蚕丝蛋白基智能可穿戴器件的功能集成与性能优化研究提供理论指导与技术框架。 Fibroin ; Wearable computer ; Electrically conductive ; Wearable technology ; SILK Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications