jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoyang Liu | Dongyan Fan | Xu Li | Tao Du | Kezhen Qi | Dayi Qian | Jianbin Song
Source: Journal of Green Construction Technology (article, 2026-02-05)
Field: Materials Science — Polymers and Plastics

Non-isothermal Crystallization Behaviors of Coal Gangue/PBAT Composites Prepared via Solution Blending In order to lessen the environmental pollution caused by coal gangue and facilitate its utilization in the polymer industry, a study on the melting and crystallization behaviors of coal gangue/Polybutylene adipate terephthalate (PBAT) composites was conducted using a differential scanning calorimetry (DSC). The results indicate that a small amount of coal gangue enhances nucleation ability, perfection degree of PBAT crystals, and accelerates PBAT crystallization rate. However, excessive coal gangue hinders PBAT crystallization. These outcomes are confirmed by Crystallization Rate Coefficient (CRC) and Mandelkern method. Crystallization ; Coal ; Nucleation ; Differential scanning calorimetry ; Polymer ; Gangue Polymer crystallization and properties ; Polymer Nanocomposites and Properties ; Fiber-reinforced polymer composites
Authors: Binu Varghese | Sipra Mohapatra | Subhadeep Dasgupta | An S | Rohan Joydhar | Annervaz K M | Subhashis Sengupta | Prabal K Maiti
Source: ChemRxiv (article, 2026-02-10)
Field: Chemistry — Inorganic Chemistry

Gas Adsorption &amp; Selectivity in Five Classes of MOFs: From CO2 to Hydrocarbon Separation The rational selection of metal organic frameworks for gas separation applications remains challenging due to the complex interplay between framework topology, pore chemistry, size, and intermolecular interactions under operating conditions. In this study, a comprehensive simulation-based screening is carried out to evaluate the gas adsorption and separation performance of five MOFs, namely IRMOF-1, HKUST-1, ZIF-8, ZIF-68, and ZIF-69. Industrially relevant systems, including CO2/CH4, N2/O2/H2, and C2H4/C2H6, are systematically investigated. Molecular simulations are performed across the pressure range, 1-40 bar for pure components and binary mixtures at 298K to assess adsorption capacity and selectivity. Notably, the selectivity was sensitive to the molar ratio and pressure, underlining the importance of evaluating MOFs across a broad range of conditions for industrial relevance. For CO2 − light gas mixtures, ZIF-68, 69 showed a non-monotonic dependence of selectivity on molar concentration. In contrast, for binary mixtures of light hydrocarbons (C2H4/C2H6), all MOFs exhibited poor selectivity across all concentrations and pressures, with little variation in performance. This insensitivity suggests the limited capability of the selected MOFs to distinguish 1 between structurally similar hydrocarbon gases, which is in stark contrast to their behavior for light gas separations. Selectivity ; Adsorption ; Hydrocarbon ; Hydrocarbon mixtures ; Intermolecular force ; Metal-organic framework ; Bar (unit) ; Gas separation Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Konstantin Sozykin | Nikita Rybin | Andrei Chertkov | Anh-Huy Phan | Ivan Oseledets | Alexander Shapeev | Ivan Novikov | Gleb Ryzhakov
Source: ArXiv.org (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Global Optimization of Atomic Clusters via Physically-Constrained Tensor Train Decomposition The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with system size (i.e., the curse of dimensionality). We introduce a novel framework that overcomes this limitation by exploiting the low-rank structure of potential energy surfaces through Tensor Train (TT) decomposition. Our approach combines two complementary TT-based strategies: the algebraic TTOpt method, which utilizes maximum volume sampling, and the probabilistic PROTES method, which employs generative sampling. A key innovation is the development of physically-constrained encoding schemes that incorporate molecular constraints directly into the discretization process. We demonstrate the efficacy of our method by identifying global minima of Lennard-Jones clusters containing up to 45 atoms. Furthermore, we establish its practical applicability to real-world systems by optimizing 20-atom carbon clusters using a machine-learned Moment Tensor Potential, achieving geometries consistent with quantum-accurate simulations. This work establishes TT-decomposition as a powerful tool for molecular structure prediction and provides a general framework adaptable to a wide range of high-dimensional optimization problems in computational material science. Maxima and minima ; Global optimization ; Curse of dimensionality ; Tensor (intrinsic definition) ; Discretization ; Probabilistic logic ; Range (aeronautics) ; Decomposition ; Optimization problem Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Binu Varghese | Sipra Mohapatra | Subhadeep Dasgupta | An S | Rohan Joydhar | Annervaz K M | Shubhashis Sengupta | Prabal K Maiti
Source: ChemRxiv (article, 2026-02-17)
Field: Chemistry — Inorganic Chemistry

Gas Adsorption &amp; Selectivity in Five Classes of MOFs: From CO2 to Hydrocarbon Separation The rational selection of metal organic frameworks for gas separation applications remains challenging due to the complex interplay between framework topology, pore chemistry, size, and intermolecular interactions under operating conditions. In this study, a comprehensive simulation-based screening is carried out to evaluate the gas adsorption and separation performance of five MOFs, namely IRMOF-1, HKUST-1, ZIF-8, ZIF-68, and ZIF-69. Industrially relevant systems, including CO2/CH4, N2/O2/H2, and C2H4/C2H6, are systematically investigated. Molecular simulations are performed across the pressure range, 1-40 bar for pure components and binary mixtures at 298K to assess adsorption capacity and selectivity. Notably, the selectivity was sensitive to the molar ratio and pressure, underlining the importance of evaluating MOFs across a broad range of conditions for industrial relevance. For CO2 − light gas mixtures, ZIF-68, 69 showed a non-monotonic dependence of selectivity on molar concentration. In contrast, for binary mixtures of light hydrocarbons (C2H4/C2H6), all MOFs exhibited poor selectivity across all concentrations and pressures, with little variation in performance. This insensitivity suggests the limited capability of the selected MOFs to distinguish 1 between structurally similar hydrocarbon gases, which is in stark contrast to their behavior for light gas separations. Selectivity ; Adsorption ; Hydrocarbon ; Hydrocarbon mixtures ; Intermolecular force ; Metal-organic framework ; Bar (unit) ; Gas separation Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Bui Quynh Anh Nguyen | Thi Phuong Loan Pham | H. Nguyen | Dieu Linh Dinh | Tri Hieu Pham | Huu Dat Nguyen | Thanh Tung Tran | The‐Bao Luong | Bui Anh Duy Nguyen | Quoc Phu Phan
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Synthesis of styrene polymer grafted nanocarbon particles for application as reinforcement additive for unsaturated polyester plastics The reinforcement of filler materials into an unsaturated polyester (UPE) matrix is often limited by agglomeration, leading to poor compatibility. Recently, nano-sized filler materials have attracted attention as effective reinforcement agents in UPE. In this study, styrene polymer-grafted carbon nanoparticles were synthesized using the solution plasma method and applied as reinforcing additives for UPE. The synthesis of nanoparticles was conducted directly in the styrene monomer environment, enabling the simultaneous formation of carbon nanoparticles and the grafting of polymer onto their surfaces <i>via</i> a free-radical mechanism. SEM analysis revealed more uniform particle dispersion and a polymer-coated surface in UPE/NC-gP samples, indicating increased compatibility with the matrix. FTIR also showed that the representative peaks C-H, C[double bond, length as m-dash]C, C[double bond, length as m-dash]O, and C-O oscillated clearly in the UPE/NC-gP samples. Notably, the use of styrene-grafted carbon nanoparticle significantly improved the tensile modulus, tensile strength, flexural modulus, and flexural strength of the UPE/NC-gP(1 h) sample to 479.89 ± 26.22, 39.23 ± 2.06, 1504.06 ± 55.18, and 44.67 ± 1.82 MPa, respectively, compared with UPE/SM. Meanwhile, the UPE/NC-gP(3 h) sample had the highest flexural stress and flexural modulus of 49.01 ± 3.03 and 1535.81 ± 58.47 MPa, respectively, but the flexural strain was low. The results indicate that the grafting time influences the properties of the UPE composite. This study confirms the potential of the solution plasma method for the fabrication of nanoadditives highly compatible with the polymer matrix, opening new avenues for the production of nanocomposite materials. Unsaturated polyester ; Styrene ; Polyester ; Polymer ; Reinforcement Polymer crystallization and properties ; Fiber-reinforced polymer composites ; Polymer Nanocomposites and Properties
Authors: Yves Grandjean | David Kreutter | Jean-Louis Reymond
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data augmentation in a triple transformer loop retrosynthesis model Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits their usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM → P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM + R by TTL transformer T3. We generated up to 5000 reactions per template, resulting in 27.5m validated fictive reactions covering the chemical space of the original USPTO dataset. To exemplify the use of this dataset, we demonstrate that a single-step retrosynthesis transformer model trained on a template equilibrated subset of 1 097 374 fictive reactions outperforms the corresponding model trained on USPTO reactions only. Retrosynthetic analysis ; Transformer ; Template ; Quadratic equation ; Loop (graph theory) Machine Learning in Materials Science ; SAS software applications and methods ; Computational Drug Discovery Methods
Authors: Abhishek Kumar | Alankar Shrivastav | Umesh Kumar | Shobhit Shrivastava | Rajesh Kumar Sharma | Suresh Babu Kondaveeti | Prajakta Chawale | M. K. Gupta | Shobana Chandrasekar
Source: Oriental Journal Of Chemistry (article, 2026-02-28)
Field: Chemistry — Organic Chemistry

Development of Quinoline Linked Hybrid: Synthesis Characterization and Antiparasitic Potential Parasitic diseases have been a significant issue to world health especially in the tropical and subtropical areas, where the existing chemotherapeutic agents have proved to have limitations such as drug resistance, drug toxicity, and poor efficacy thus rendering disease control ineffective. In this regard, quinoline-based compounds have remained of significant interest based on their established antiparasitic and long-standing clinical efficacy. The current review gives an overview of the quinoline-linked hybrid molecules as the future antiparasitic agents with a focus on their design, synthesis procedures, characterizations, and biological performance. Quinoline is a privileged scaffold that possesses desirable chemical and pharmacological properties and by hybridizing with various partner pharmacophores, is a rational approach to increase potency, expand target interactions, and decrease resistance liability. The major synthetic strategies, starting with the classical quinoline-forming reactions, all the way to the current coupling, click chemistry and green methodology are addressed in the framework of efficient hybrid construction. The review also discusses in vitro and in vivo antiparasitic evaluation plans, the use of standardized measurements of potency, the evaluation of selectivity, pharmacokinetics and safety profiling. Mechanistic data on the antiparasitic activity such as inhibition of heme detoxification, impairment of mitochondrial activity, stimulation of oxidative stress, synergetic multi-target effects by partner pharmacophores is critically examined. Lastly, the existing issues, future opportunities and translation concerns are discussed, to inform rational optimization and clinical improvement. Taken together, this review highlights the potential of quinoline-based hybrids as a flexible and potent platform towards development of next-generation antiparasitic therapeutics. Antiparasitic ; Antiparasitic agent ; Pharmacophore ; Drug discovery ; Drug development ; Drug ; ADME Synthesis and biological activity ; Malaria Research and Control ; Multicomponent Synthesis of Heterocycles
Authors: Kun Li | Lening Hu | Yida Xiong | Jiajun Yu | Hongzhi Zhang | Jiameng Chen | Xiantao Cai | Jia Wu | Wenbin Hu
Source: ArXiv.org (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary Molecular representation learning aims to learn vector embeddings that capture molecular structure and geometry, thereby enabling property prediction and downstream scientific applications. In many AI for science tasks, labeled data are expensive to obtain and therefore limited in availability. Under the few-shot setting, models trained with scarce supervision often learn brittle structure-property relationships, resulting in substantially higher prediction errors and reduced generalization to unseen molecules. To address this limitation, we propose PCEvo, a path-consistent representation method that learns from virtual paths through dynamic structural evolution. PCEvo enumerates multiple chemically feasible edit paths between retrieved similar molecular pairs under topological dependency constraints. It transforms the labels of the two molecules into stepwise supervision along each virtual evolutionary path. It introduces a path-consistency objective that enforces prediction invariance across alternative paths connecting the same two molecules. Comprehensive experiments on the QM9 and MoleculeNet datasets demonstrate that PCEvo substantially improves the few-shot generalization performance of baseline methods. The code is available at https://anonymous.4open.science/r/PCEvo-4BF2. Generalization ; Representation (politics) ; Property (philosophy) ; Dependency (UML) ; Evolutionary algorithm ; Code (set theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Azza Fadhel | Yassine Chemingui | Minh Hoang | Aryan Deshwal | Trong Nghia Hoang | Jana Doppa
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Nanoporous Materials Discovery via Search Bias-Guided Surrogate Modeling Nanoporous materials (NPMs) are suitable for solving some of the society's biggest challenges including carbon capture and conversion, storing hydrogen and methane, and sensing gases. The key challenge in discovering high-performing NPMs for a target application is that making and evaluating candidate NPMs requires performing resource-expensive wet-lab experiments. We consider the problem of discovering NPMs using existing experimental data of NPM evaluations. The overall goal is to find better NPMs than the best NPMs from the past experimental data. A simple approach is to create a surrogate model to match the objective values on the given dataset and employ it to score candidate NPMs to discover optimized NPMs. However, this surrogate model will fail because it does not have the appropriate search bias for the goal of optimization. To address this challenge, we propose a novel surrogate modeling approach that combines value matching loss with an optimization bias as regularizer. The key idea is to algorithmically realize search bias is to mimic the search behavior of monotonically increasing sequences of NPMs from the given dataset. Experiments on multiple real-world NPM discovery tasks demonstrate that our proposed surrogate model discovers significantly better NPMs than baselines including value matching surrogate model and one-step Bayesian optimization. Key (lock) ; Matching (statistics) ; Surrogate model ; Bayesian probability ; Bayesian optimization ; Value (mathematics) ; Representation (politics) Machine Learning in Materials Science ; Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures
Authors: Maria Angeline Magalong | Shayne Ruzzel Galvez | Kristine Oira Flordeliza | Jenuelle Lui Caballero | Peniel Jean Gildo | Rugi Vicente Rubi
Source:  (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Optimization of Ultrasound-Assisted Solvothermal Synthesis of N-Doped Carbon Dots Derived from Water Hyacinth (Pontederia crassipes) for Carbon Monoxide Sensing Carbon monoxide (CO) is an odorless, colorless, and toxic gas that requires effective detection due to health risks upon exposure. This study investigates the synthesis of nitrogen-doped carbon dots (N-CDs) from water hyacinth using an ultrasound-assisted solvothermal method for CO sensing. A Box–Behnken design under response surface methodology (RSM) optimized the synthesis parameters at 177 C, 6.25 h, and 2.62 g dopant, achieving a maximum quantum yield of 20.15%. UV-vis and PL analysis confirmed successful nitrogen doping and stable excitation-independent photoluminescence. FESEM-EDX revealed spherical to quasi-spherical particles ranging from 8 to 55 nm with carbon, nitrogen, and oxygen composition. Gas sensing results revealed enhanced CO response for N-doped CDs compared to undoped CDs due to improved charge transfer and increased adsorption sites, demonstrating their potential for CO detection at low concentrations. Solvothermal synthesis ; Carbon monoxide ; Adsorption ; Carbon fibers ; Yield (engineering) ; Quantum dot ; Oxygen ; Hyacinth ; Quantum yield Carbon and Quantum Dots Applications ; Gas Sensing Nanomaterials and Sensors ; Electrochemical sensors and biosensors
Authors: Jumin Qiu | Tingting Liu | Chenxuan Xiang | Tianbao Yu | Qiegen Liu | Shuyuan Xiao
Source: Optica (article, 2026-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrated spectropolarimeter by metasurface-based diffractive optical networks Conventional spectrometer and polarimeter systems rely on bulky optics, limiting compact integration and hindering multi-dimensional optical sensing capabilities. Here, we propose a spectropolarimeter enabled by metasurface-based diffractive optical networks that simultaneously perform spectrometric and polarimetric measurements in a compact device. By leveraging the wavelength- and polarization-dependent phase modulation of metasurfaces, our system encodes the spectral and polarization information of incident light into spatially resolved intensity distributions, which are decoded by a co-designed deep neural network, enabling simultaneous high-accuracy reconstruction of spectral compositions and Stokes parameters through a single-shot measurement. Experiments validate the method’s accurate reconstruction of the spectral and polarization information across a broad wavelength range and further confirm its imaging capability. Notably, we demonstrate a chip-integrated sensor prototype combining both measurement functionalities into a commercial image sensor. This integrated platform provides a compact solution for on-chip multi-dimensional optical sensing, holding potential for versatile sensing, biomedical diagnosis, and industrial metrology. Optical filter ; Beam splitter ; Polarization (electrochemistry) ; Diffraction Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Polarization and Ellipsometry
Authors: Achinthya Krishna Bheemaguli | Penghao Xiao | Gopalakrishnan Sai Gautam
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of foundational machine learned interatomic potentials for migration barrier predictions We benchmark foundational MLIPs against DFT-NEB calculated migration barriers and the corresponding accuracy of intermediate geometries. We also highlight the possible correlation between the accuracy of barrier and geometry predictions. Benchmark (surveying) ; Interpolation (computer graphics) ; Set (abstract data type) ; Work (physics) ; Ionic bonding ; Data point ; Electrical conductor ; Base (topology) ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Seongwoo Kang | Shinyeong Park | Jiwon Chang
Source: Journal of Materials Chemistry C (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical screening of Co- and Mo-based binary alloys as interconnect metals This work presents the candidate screening procedure of binary intermetallic compounds for future metal interconnects using cohesive energy, and directionally averaged figure of merit. Intermetallic ; Binary number ; Interconnection ; Work (physics) ; Metal Copper Interconnects and Reliability ; Semiconductor materials and interfaces ; Electrodeposition and Electroless Coatings
Authors: Kapil Goyal | Aditi Vishwakarma Anya Jain | Nisha Rathi Abhishek Verma | Ashish Anjana
Source: International Journal of Advancement and Innovation in Technology and Research (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

MilkoSense: A Rapid AI-Assisted Milk Quality Testing System for Enhanced Dairy Safety and Efficiency Quality (philosophy) ; Raw milk ; Food safety ; Milk products Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Smart Agriculture and AI
Authors: Seonghyeon An | Jaewon Chang | Yeongjoo Suh | Yuni Park | Sanghwa Jeong
Source: Small Methods (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Semi‐Automated and Consistent Fabrication Method of ssDNA‐Functionalized Single‐Walled Carbon Nanotube Biosensor Single-walled carbon nanotubes (SWCNTs) exhibit exceptional optical and electronic properties but require proper dispersion in a liquid for solution process and biological applications. Here, we present a semi-automated platform for consistent fabrication of ssDNA-wrapped SWCNT dispersions. Conventional manual preparation methods suffer from batch-to-batch variations due to imprecise SWCNT weighing and inconsistent sonication parameters. Our system integrates an XYZ-motorized stage with a programmable tip sonicator and the preparation of SWCNT mass source with high consistency, enabling unprecedented consistency and automated processing of multiple samples. This platform enables systematic investigation of fabrication parameters, including ssDNA:SWCNT mass ratio, sonication power, and duration. We evaluate the performance of these hybrids as serotonin nanosensors, analyzing their fluorescence response characteristics and sensitivity. Our approach significantly reduces human labor while providing precise control over critical parameters, yielding consistent, high-quality serotonin-responsive nanosensors. This research demonstrates the importance of automated fabrication processes for achieving reliable and scalable production of SWCNT-based nanomaterials with uniform sensing properties. Fabrication ; Carbon nanotube ; Sonication ; Nanomaterials ; Dispersion (optics) ; Process (computing) ; Biosensor Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors ; Carbon and Quantum Dots Applications