jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wendy Huang | Yucheng Jin | Li Z | Lin Yao | Yun Chen | Zheng Luo | Shen Zhou | Jinguo Lin | Feng Liu | Zhifeng Gao | Jun Cheng | Linfeng Zhang | Fangping Ouyang | Jin Zhang | Shanshan Wang
Source: Nature Communications (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Auto-resolving the atomic structure at van der Waals interfaces using a generative model The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materials. However, a rapid, accurate, and robust approach to automatically resolve complex patterns in atomic-resolution microscopy remains difficult to implement. Here, we present a Trident strategy-enhanced disentangled representation learning method (a generative model), which utilizes a few unlabelled experimental images with abundant low-cost simulated images to generate a large corpus of annotated simulation data that closely resembles experimental results, producing a high-quality large-volume training dataset. A structural inference model is then trained via a residual neural network which can directly deduce the interlayer slip and rotation of diversified and complicated stacking patterns at van der Waals (vdW) interfaces with picometer-scale accuracy across various materials (e.g. MoS<sub>2</sub>, WS<sub>2</sub>, ReS<sub>2</sub>, ReSe<sub>2</sub>, and 1 T'-MoTe<sub>2</sub>) with different layer numbers (bilayer and trilayers), demonstrating robustness to defects, imaging quality, and surface contaminations. The framework can also identify pattern transition interfaces, quantify subtle motif variations, and discriminate moiré patterns that are difficult to distinguish in frequency domains. Finally, the high-throughput processing ability of our method provides insights into a vdW epitaxy mode where various thermodynamically favorable slip stackings can coexist. van der Waals force ; Generative grammar ; Biomolecular structure ; Computer science ; Physics ; Van der Waals radius ; Statistical physics ; Chemical physics ; Protein structure ; Quantum mechanics ; Artificial intelligence ; Molecule Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Surface Chemistry and Catalysis
Authors: Huijie Jia | Chenlin Wu | Min Huang | Qibing Zhu
Source: International Journal of Biological Macromolecules (article, 2025-01-18)
Field: Chemistry — Analytical Chemistry

An intelligent fruit freshness monitoring system using hydrophobic indicator labels based on methylcellulose, k-carrageenan, and sodium tripolyphosphate, combined with deep learning Chemistry ; Sodium ; Carrageenan ; Food science ; Chemical engineering ; Chromatography ; Organic chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Md. Shakhawat Hossen | Mohammad Tariqul Islam | Phumin Kirawanich | Ahasanul Hoque | Abdulmajeed M. Alenezi | Mohd Hafiz Baharuddin | Haitham Alsaif | Mohamed S. Soliman
Source: Optics & Laser Technology (article, 2025-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

500HN polyimide film sandwich metamaterial absorber with enhanced sensing capabilities and assisted machine learning absorption forecasting Polyimide ; Materials science ; Absorption (acoustics) ; Metamaterial ; Metamaterial absorber ; Composite material ; Optoelectronics ; Computer science ; Tunable metamaterials ; Layer (electronics) Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Energy Harvesting in Wireless Networks
Authors: Shuang Zhang | Zhixiao Xu | Youming Chen | Lai Jiang | Aiting Wang | Guangxia Shen | Xianting Ding
Source: ACS Nano (article, 2025-01-22)
Field: Chemistry — Spectroscopy

Lanthanide Metal–Organic Framework Flowers for Proteome Profiling and Biomarker Identification in Ultratrace Biofluid Samples Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination. We further developed a <b>M</b>OF-flower <b>a</b>ssisted <b>s</b>implified and <b>s</b>ingle-pot <b>S</b>ample <b>P</b>reparation (Mass-SP) workflow that incorporates protein capture, digest, and peptide elute into one single PCR tube to maximally avoid adsorptive sample loss. We adopted Mass-SP to decipher aqueous humor (AH) proteome signatures from cataract and retinal vein occlusion (RVO) patients and quantified ∼3900 proteins in merely 1 μL of AH. Combined with machine learning, we further identified PFKL as a prioritization biomarker for RVO disease with the areas under the curves of 0.95 ± 0.04. Mass-SP presents a strategy to identify de novo biomarkers and explore potential therapeutic targets with extremely limited clinical human body fluid resources. Proteome ; Biomarker ; Lanthanide ; Profiling (computer programming) ; Chemistry ; Nanotechnology ; Environmental chemistry ; Chromatography ; Computational biology ; Materials science ; Biology ; Biochemistry ; Computer science ; Organic chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Abdulrazak Jinadu Otaru
Source: Case Studies in Thermal Engineering (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Circular economy: Kinetic-Triplet, thermodynamic, and gradient descent optimisation algorithm of deep learning models for the thermal degradation of walnut shell The study offers a thorough assessment of the thermal degradation of pulverised walnut shell, utilizing thermo-kinetic analysis and a gradient descent optimisation algorithm. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) measurements were obtained at heating rates of 3, 10, 20, and 40 °C.min −1 , with degradation temperatures ranging from 30 to 850 °C. The thermo-kinetic analysis of the TGA traces yielded activation energies of 89.33, 83.71, 84.12, and 92.26 kJ mol −1 , determined through the FWO, KAS, STK, and FR isoconversional model-free kinetic methods, respectively. The power law [P3] nucleation model was identified as the likely reaction mechanism. The estimated thermodynamic data indicate that the formation of activated complexes from the reactants is endothermic (Activation enthalpy, ΔH = 78.13–86.68 kJ mol −1 ), non-spontaneous (Gibbs free energy of activated complex, ΔG = 184.13–288.05 kJ mol −1 ), and less disordered (Entropy of activation, ΔS = −0.31 to −0.16 kJ mol −1 K −1 ), positioning walnut shell as a more viable bioenergy source compared to various agricultural wastes. The application of the gradient descent optimisation algorithm resulted in an optimized deep neural network (DNN) framework and parameters that produced output signals closely aligning with experimental measurements ( R 2 ∼ 0.999 ). This study identified the degradation temperature as the most sensitive parameter influencing the thermal degradation of walnut shell. The findings could provide valuable insights for energy and environmental experts, as well as policymakers, regarding the potential use of walnut shell as a viable feedstock for pyrolysis and bioenergy production. Thermal ; Kinetic energy ; Degradation (telecommunications) ; Descent (aeronautics) ; Gradient descent ; Kinetics ; Thermodynamics ; Computer science ; Materials science ; Algorithm ; Physics ; Artificial intelligence ; Artificial neural network ; Classical mechanics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Spectroscopy and Chemometric Analyses
Authors: Frage Abookleesh | Muhammad Zubair | Aman Ullah
Source: Chemical Engineering Journal (article, 2025-04-14)
Field: Materials Science — Biomaterials

Leveraging machine learning for the optimization of reinforced rapeseed protein-gelatin edible coatings for enhanced food preservation • Expanded 13 data points to 1,000 using Gaussian noise for data augmentation. • ML models showed R 2 : 0.9517–0.9998 and MSE: 0.0001–0.5537. • Optimized film had T.S. 28.94 MPa, E% 22.61, and WVP 4.40 × 10 2 g·mm/m 2 ·day·kPa. • Model predictions showed strong correlation with experimental results. • Edible film coating extended fruit shelf life by 7 days at ambient conditions. Optimizing component ratios is crucial for enhancing bioplastic formulations. Traditional optimization is time-consuming, while data-driven methods require large datasets. To address this, we augmented thirteen experimental data points to 1,000, enhancing machine learning (ML) model accuracy. Three ML models were trained to optimize the formulation of a rapeseed protein-gelatin nanocomposite reinforced with cellulose nanocrystals (CNC) and cross-linked with citric acid. The models showed high predictive performance (R 2 : 0.9517–0.9998; MSE: 0.0001–0.5537; MAE: 0.0018–0.4221). The optimal formulation (1:1:5.5:3.87) yielded a film with 28.94 ± 1.18 MPa tensile strength, 22.61 ± 2.12 % elongation, and 4.40 ± 0.04 g mm/d·m 2 ·kPa × 10 2 water vapor permeability. A film based on this formulation was applied as a coating on fruits and showed an increased shelf life, verifying its practical application. Our results show that the integration of generated data enhances ML predictive performance, capturing complex nonlinear relationships between input and output variables, thus balancing the mechanical and barrier properties of biopolymer films. This work highlights the potential of data generation and ML in materials science by providing a pathway to accelerate the development of sustainable bio-based plastics. Rapeseed ; Gelatin ; Food science ; Computer science ; Materials science ; Process engineering ; Pulp and paper industry ; Chemistry ; Engineering ; Biochemistry Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; Food and Agricultural Sciences
Authors: Herzain I. Rivera-Arrieta | Lucas Foppa
Source: ACS Catalysis (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Rules Describing CO <sub>2</sub> Activation on Single-Atom Alloys from DFT-Meta-GGA Calculations and Artificial Intelligence Single-atom alloys (SAAs) arise as a promising concept for the design of improved CO<sub>2</sub> hydrogenation catalysts. However, from the immense number of possible SAA compositions and structures, only a few might display the properties required to be useful catalysts. Thus, the direct, high-throughput screening of materials is inefficient. Here, we use artificial intelligence to derive rules describing surface sites of SAAs that provide an effective CO<sub>2</sub> activation, a crucial initial step to convert the molecule into valuable products. We start by modeling the CO<sub>2</sub> interaction with 780 sites of flat and stepped surfaces of SAAs composed by Cu, Zn, and Pd hosts via high-quality DFT-mBEEF calculations. Then, we apply subgroup discovery to determine constraints on key physicochemical properties, out of 24 offered candidate descriptive parameters, characterizing subgroups (SGs) of surface sites where chemisorbed CO<sub>2</sub> displays large elongations of its C-O bonds. The key identified parameters are free-atom properties of the elements constituting the surface sites, such as their electron affinity, electronegativity, and radii of the d-orbitals. Additionally, the generalized coordination number is selected as a key geometrical parameter. The SG rules are applied to identify promising surface sites from a candidate space of over 1500 possible ones in different single-atom and dual-atom alloys. Some of the promising alloys predicted by the SG rules were explicitly tested by additional DFT-mBEEF calculations and confirmed to provide a significant CO<sub>2</sub> activation. Atom (system on chip) ; Catalysis ; Materials science ; Density functional theory ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Computer science Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Danish Arif | Rajwali Khan | Atta Ullah Shah | Kashif Safeen | Khalid M. Alotaibi | Hayat Ullah | Muhammad Zia Ullah Shah | Wubshet Mekonnen Girma | Akif Safeen
Source: Scientific Reports (article, 2025-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of ZnO–VSe2 nanocomposite for high performance asymmetric supercapacitors Supercapacitors exhibit limitations such as low energy density, high self-discharge rates, and degradation of electrochemical performance over extended cycling. This study presents the development of a high-performance asymmetric supercapacitor by synthesizing novel ZnO-VSe<sub>2</sub> nanocomposites through wet-chemical methods, aiming to enhance capacity, energy density, and durability. The capacitive performance of these materials was systematically evaluated in an aqueous alkaline electrolyte (KOH) at a concentration of 2 M. ZnO-VSe<sub>2</sub> composite demonstrates superior electrochemical energy storage capabilities, achieving a specific capacitance of 898 F/g and reducing overall resistance, enabling rapid electrolyte diffusion. These optimized electrochemical characteristics underscore the potential of ZnO-VSe<sub>2</sub> for energy storage applications. Specifically, the ZnO-VSe<sub>2</sub>||AC asymmetric supercapacitor achieved an impressive capacitance of 260 F/g, an energy density of 71 Wh/kg, and a maximum power output of 6948 W/kg, along with a remarkable stability of 89.1% at a current density of 10 A/g over 5000 cycles. The proposed methodology offers a cost-effective and promising approach for evolving high-energy hybrid supercapacitors for energy storage applications. Supercapacitor ; Nanocomposite ; Materials science ; Nanotechnology ; Computer science ; Chemistry ; Capacitance ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Thanh Van Phan | Thi Xuan Nguyen | Thi Thuy Truong | Thi Kim Ngan Duong | Vu Hoang Minh Doan | Truong Tien Vo | Mrinmoy Misra | Jaeyeop Choi | Umapada Pal | Junghwan Oh | Sudip Mondal
Source: SmartMat (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Advancements, Challenges, and Future Perspectives of MXenes in Biomedicine ABSTRACT MXenes, a unique class of two‐dimensional (2D) transition metal carbides, nitrides, and carbonitrides, have garnered significant interest due to their exceptional chemical, mechanical, and electrical properties. While recent studies predominantly focus on MXenes' applications in catalysis, energy storage and harvesting, photocatalysis, and lightweight materials, their potential in biomedicine is comparatively understated. This review aims to bridge this gap by providing a comprehensive and up‐to‐date overview of MXenes in biomedical applications, specifically highlighting advanced uses such as photothermal therapy and photodynamic therapy for cancer treatment, as well as their roles in biomedical imaging and as contrast agents for tumor visualization. We examine the synthesis and chemical modifications of MXenes, including functionalization, etching, and exfoliation techniques that enable tailored properties for biomedicine. This article highlights MXenes' advantages, including high surface area, tunable surface chemistry, and biocompatibility, while also addressing challenges and future research directions to unlock their full biomedical potential. This focused exploration of MXenes in cutting‐edge biomedicine sets this review apart, highlighting its significance in advancing MXenes' role in modern biomedical research. MXenes ; Biomedicine ; Engineering ethics ; Engineering ; Nanotechnology ; Biology ; Bioinformatics ; Materials science MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; 2D Materials and Applications
Authors: Shenning Liu | Yuxian Wang | Ya Liu | Peihan Chen | Tao Kong | Xiaoguang Duan | Chunmao Chen | Hongqi Sun | Shaobin Wang
Source: Nature Communications (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Tailored ozone activation on geometrical-site-dependent cobalt with selective coordination Cobalt-containing spinel oxides are promising platforms to fine-tune the intrinsic activity/selectivity of their geometric sites in catalysis. However, the role of tetrahedrally occupied Co<sup>2+</sup> (Co<sup>2+</sup><sub>Td</sub>) and Co<sup>3+</sup> in an octahedral site (Co<sup>3+</sup><sub>Oh</sub>) in controlling the catalytic activity remains controversial. Herein, we investigated a geometrical-site-dependent catalytic activation of ozone respectively on the Co<sup>2+</sup><sub>Td</sub> and Co<sup>3+</sup><sub>Oh</sub> sites. The same exposure of [111] crystal facet is achieved by substituting those undesired sites with catalytically inactive cations. The highly spin-polarized Co<sup>2+</sup><sub>Td</sub> sites invoke strong orbital interactions and intensive electron transfer with the adsorbed O<sub>3</sub> and become the active sites for selectively producing surface-bound hydroxyl radicals (<sup>•</sup>OH) and avoiding the formation of unfavorable singlet oxygen (<sup>1</sup>O<sub>2</sub>), resulting in a 17.6-fold increase in turnover frequency (TOF). This work enlightens the spin-polarized electronic states into regulating the reaction thermodynamics in transition metal oxide-induced catalysis and envisages the practical application potentials of geometric site engineered spinel oxides. Cobalt ; Ozone ; Chemistry ; Inorganic chemistry Catalytic Processes in Materials Science
Authors: Mohammad Amin Akhound | Karsten W. Jacobsen | Kristian S. Thygesen
Source: Journal of the American Chemical Society (preprint, 2025-02-07)
Field: Materials Science — Materials Chemistry

Activating the Basal Plane of 2D Transition Metal Dichalcogenides via High-Entropy Alloying Two-dimensional materials, such as transition metal dichalcogenides (TMDCs) in the 2H or 1T crystal phases, are promising (electro)catalyst candidates due to their high surface-to-volume ratio and composition of low-cost, abundant elements. While the edges of elemental TMDC nanoparticles, such as MoS<sub>2</sub>, can show significant catalytic activity, the basal plane of the pristine materials is notoriously inert, which limits their normalized activity. Here, we show that high densities of catalytically active sites can be formed on the TMDC basal plane by alloying elements that prefer the 2H (1T) phase into a 1T (2H) structure. The global stability of the alloy, in particular, whether it crystallizes in the 2H or 1T phase, can be controlled by ensuring a majority of elements prefer the target phase. We further show that the mixing entropy plays a decisive role in stabilizing the alloy, implying that high-entropy alloying becomes essential. Our calculations point to a number of interesting nonprecious hydrogen evolution catalysts, including (CrTaVHfZr)S<sub>2</sub> and (CrNbVTiZr)S<sub>2</sub> in the 1T-phase and (MoNbTaVTi)S<sub>2</sub> in the 2H-phase. Our work opens new directions for designing catalytic sites via high-entropy alloy stabilization of locally unstable structures. Alloy ; Materials science ; Transition metal ; Catalysis ; High entropy alloys ; Basal plane ; Metal ; Nanotechnology ; Entropy (arrow of time) ; Inert ; Phase transition ; Chemical physics ; Chemical engineering ; Crystallography ; Metallurgy ; Thermodynamics ; Chemistry ; Physics ; Organic chemistry 2D Materials and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Mohamed A. Abdel-Fadeel | Sherief A. Al Kiey | Peter Hesemann | Ahmad Mehdi | Samir Kamel | Ahmed K. El‐Ziaty | Naglaa Salem El‐Sayed
Source: International Journal of Biological Macromolecules (article, 2025-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From sugarcane bagasse pulp into electroactive materials: Carboxymethyl cellulose/reduced graphene oxide/nickel oxide composites as high-performance supercapacitor components We report bio-sourced electrically conductive materials derived from sugarcane bagasse cellulose. First, the cellulose was chemically transformed into carboxymethyl cellulose (CMC). Acrylamide (AM) and 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS) were co-polymerized with CMC, and the resulting material was cross-linked with N,N`-methylenebisacrylamide (MBA) to improve its mechanical properties. To further enhance the electrical performance, the materials were modified with reduced graphene oxide (rGO), nickel oxide (NiO) nanoparticles and rGO@NiO composites. The nanocomposites were then used as electrodes for solid-state supercapacitors. To evaluate the electrochemical properties of these materials, cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) were performed. These results demonstrate enhanced electrochemical performance of the composite material modified with both rGO and NiO NPs. CV profiles exhibited well-defined redox peaks indicative of reversible faradaic processes, confirming high pseudocapacitance contributions. The optimized sample X<sub>GNi2.5</sub> exhibited a high specific capacitance of 497.8 F g<sup>-1</sup> at 0.5 A g<sup>-1</sup>, outstanding cycling stability with 91 % capacitance retention after 5000 cycles, and a maximum energy density of 44.2 Wh kg<sup>-1</sup> at a power density of 226.2 W kg<sup>-1</sup>. EIS revealed low charge transfer resistance and efficient ion transport. These results underscore the potential of CMC/rGO/NiO composites as sustainable, high-performance electrode materials for next-generation supercapacitors. Bagasse ; Carboxymethyl cellulose ; Graphene ; Supercapacitor ; Materials science ; Nickel ; Oxide ; Cellulose ; Pulp (tooth) ; Nickel oxide ; Composite material ; Pulp and paper industry ; Chemistry ; Electrode ; Metallurgy ; Capacitance ; Nanotechnology ; Organic chemistry Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials