jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samin Motallebzadeh | Leila Ahmadian-Alam | Masoumeh Bahrami | Arturo Andrade | Edward Song
Source: The Analyst (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Continuous monitoring of glutamate using electroactive templated polymers as synthetic molecular receptors Selective and spatiotemporally resolved monitoring of glutamate in the brain is essential for understanding its role in many brain functions as well as in the progression of various mental disorders. However, achieving accurate time resolved glutamate detection has been challenging due in part to the lack of glutamate-binding receptors that can offer both target selectivity and continuous measurement capability. To address this challenge, we have developed a novel polymer-based electrochemical biosensor designed to enhance selectivity for glutamate. Our proposed biosensor incorporates an innovative templated polymer-based target receptor, which selectively binds to the glutamate molecule. Furthermore, the reversible binding kinetics enable continuous glutamate detection with a time resolution of approximately 1 minute and a detection limit of 88.5 nM in artificial cerebrospinal fluid (ACSF) buffer. Additionally, due to the synthetic target-imprinted receptors, the sensor exhibited high selectivity for glutamate in the presence of other interfering neurochemicals GABA, glycine, and aspartate. These results indicate that the proposed sensor technology holds potential for monitoring glutamate in real physiological samples with possible use in clinical settings. Glutamate receptor ; Selectivity ; Biosensor ; Continuous monitoring ; Detection limit ; Biocompatible material ; Resolution (logic) ; Receptor Molecular Sensors and Ion Detection ; Polydiacetylene-based materials and applications ; Conducting polymers and applications
Authors: Abhijith S. Parackal | Rickard Armiento | Florian Trybel
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials Machine learned interatomic potentials (MLIPs) are becoming a standard method for DFT-level accurate molecular dynamics simulation and large-scale studies of crystal energetics. Increasingly popular are universal pre-trained potentials, also called foundation models, based one, e.g. the MACE, CHGNet, M3GNet, ORB, and SevenNet architectures. While there are many benchmarks of these models using validation errors and materials discovery tasks, their fidelity in reproducing the detailed features of potential energy surfaces (PES) is not understood to the same degree. We evaluate the accuracy of these potentials by systematically probing their predicted energy landscapes. Two-dimensional slices of the potential energy surface are constructed where the atomic positions are varied along selected Wyckoff degrees of freedom within a fixed crystal symmetry. This approach enables a direct, visual comparison of the interatomic potentials and DFT-calculated surfaces which reveals potential artifacts e.g., arising from unique local environments. Our analysis highlights the strengths and limitations of different potentials in capturing local minima, saddle points, and overall PES topology, offering insights into the physical accuracy of current pre-trained IAPs and providing benchmarks for future model development. Interatomic potential ; Benchmark (surveying) ; Energy (signal processing) ; Potential energy ; Molecular dynamics ; Fidelity ; Saddle point ; Degrees of freedom (physics and chemistry) Machine Learning in Materials Science ; Quantum many-body systems ; Inorganic Chemistry and Materials
Authors: Chenchen Chu | Mingyang Jiang | Yubei Zhang | Kun Feng | Chaolei Hua | Lie Wu | Yijie Chen | Ling Ji | Xitong Gao | Xue-Feng Yu | Shengyong Geng | Wu Zhou
Source: Nano-Micro Letters (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Confined Synthesis of Axial Chlorine Coordinated Single-Atom Nanozyme Within Liposomes for Sensitive Immunoassay Single-atom nanozymes (SANs) are considered as the most promising candidates for signal amplification in biosensing applications. However, maintaining the integrity of active sites during functionalization while preserving catalytic performance in biological environments remains a critical challenge. Here, we present a low-temperature-photochemical synthesis strategy for constructing platinum single-atoms within liposomes (PtSANs@Lipo), achieving atomic-level regulation of SANs active sites through axial chlorine (Cl) coordination engineering. The PtN<sub>3</sub>Cl<sub>2</sub> coordination structure, validated by synchrotron X-ray absorption spectroscopy, induces optimal d-band center modulation giving a remarkably low determining step energy barrier compared to conventional PtN<sub>3</sub> configurations. This architecture further enables non-contact functionalization via liposomal encapsulation, fully preserving catalytic activity while preventing catalytic sites occupation by antibody, thereby reducing the Michaelis constant (K<sub>m</sub>) value by 50-fold compared to direct modification onto Pt single-atoms. The constructed immunosensor based on PtSANs@Lipo demonstrates highly sensitive detection of viral pathogens, including influenza A virus H1N1, SARS-CoV-2, and influenza B virus antigens with limits of detection as low as 0.42, 2.23, and 0.36 fg mL<sup>-1</sup>, respectively. This work establishes a paradigm for bio-adaptive nanozyme design through synergistic coordination engineering and liposomal functionalization architectures, and may thus provide a universal approach for signal amplification in point-of-care diagnostics. Surface modification ; Catalysis ; Biosensor ; Liposome ; Chlorine ; Analyte ; Rational design ; Rhodium Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Xiaohan Yi | Guikun Xu | Zhong Zhang | Liu Liu | Yatao Bian | Xi Xiao | Peilin Zhao
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

CrystalDiT: Simple Diffusion Transformers for Crystal Generation We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting. Transformer ; Lattice (music) ; Representation (politics) ; Stability (learning theory) ; Discretization ; Simple (philosophy) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Lijun Zhang | Ang Zhang | Tao Jiang | Zhihua Dong | Weijun He | Jun Tan | Yu Yang | Bin Jiang
Source: Materials Genome Engineering Advances (article, 2026-01-28)
Field: Materials Science — Biomaterials

Molecular Dynamics Study of Melt Structure and Transport Properties of Mg‒Al Alloy ABSTRACT The development of magnesium alloy gigacastings puts forward higher demand on alloy fluidity because of more complex structure and longer filling distance. The fluidity is closely connected with atomic diffusion and melt viscosity. This work investigates the melt structure, self‐diffusion coefficient, and viscosity of the Mg‒6wt.%Al alloy at different temperatures through molecular dynamics simulations. The melt structure is characterized by pair distribution functions, coordination numbers, H‒A index, and Voronoi polyhedron analysis. The self‐diffusion coefficient is determined by the mean squared displacement method, whereas the viscosity is evaluated through the Green–Kubo equation and the Muller‐Plathe algorithm. By establishing relationships among structure, diffusion, and viscosity, the microscopic mechanisms behind the temperature‐induced changes in transport properties are revealed, in which the change of the degree of order at different temperatures is highlighted. The findings provide an atomic‐scale theoretical basis for understanding the rheological behavior of magnesium alloy melt. Alloy ; Rheology ; Molecular dynamics ; Mean squared displacement ; Viscosity ; Diffusion ; Voronoi diagram ; Work (physics) Magnesium Alloys: Properties and Applications ; Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties
Authors: Tri Nguyen | Sherif Abdulkader Tawfik | Truyen Tran | Svetha Venkatesh
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities Accurate charge densities are central to electronic-structure theory, but computing charge-state-dependent densities with density functional theory remains too expensive for large-scale screening and defect workflows. We present ChargeFlow, a flow-matching refinement model that transforms a charge-conditioned superposition of atomic densities into the corresponding DFT electron density on the native periodic real-space grid using a 3D U-Net velocity field. Trained on 9,502 charged Materials Project-derived calculations and evaluated on an external 1,671-structure benchmark spanning perovskites, charged defects, diamond defects, metal-organic frameworks, and organic crystals, ChargeFlow is not uniformly best on every in-distribution class but is strongest on problems dominated by nonlocal charge redistribution and charge-state extrapolation, improving deformation-density error from 3.62% to 3.21% and charge- response cosine similarity from 0.571 to 0.655 relative to a ResNet baseline. The predicted densities remain chemically useful under downstream analysis, yielding successful Bader partitioning on all 1,671 benchmark structures and high-fidelity electrostatic potentials, which positions flow matching as a practical density-refinement strategy for charged materials. Benchmark (surveying) ; Superposition principle ; Density functional theory ; Electron density ; Charge (physics) ; Grid ; Charge density ; Redistribution (election) ; Electron Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Sebnem Sözcü | Jakub Wiener | Jaroslava Frajová | Mohanapriya Venkataraman | Blanka Tomková | József Kalmár | Attila Forgács | Jiří Militký
Source: Scientific Reports (article, 2026-03-05)
Field: Materials Science — Biomaterials

Effect of drying methods on Acetobacter xylinum bacterial cellulose aerogels and cryogels Bacterial cellulose (BC) pellicles were produced from Acetobacter xylinum using a simple, additive-free, and low-cost static cultivation method consistent with sustainable and green bioprocessing principles. Two post-synthesis drying routes were compared: supercritical carbon dioxide (scCO<sub>2</sub>) drying following acetone solvent exchange and direct lyophilization without chemical additives or pre-freezing. The resulting BC aerogels and cryogels were characterized by SEM, confocal microscopy, BET analysis, FTIR spectroscopy, EDS, and geometrical evaluation with a particular emphasis on nanostructure, porosity, and network integrity. scCO<sub>2</sub>-dried BC aerogels exhibited a well-preserved three-dimensional nanofibrillar network, achieving a BET surface area (123 m<sup>2</sup>/g), large pore volume (0.36 cm<sup>3</sup>/g), and an average pore diameter of 10 nm. Confocal microscopy revealed higher surface roughness (Rz up to ~ 58 μm), reflecting a more developed and heterogeneous surface topography. Lyophilized BC cryogels showed lower surface area (51 m<sup>2</sup>/g) and pore volume (0.13 cm<sup>3</sup>/g); however, SEM and confocal analyses indicated that the nanofibrillar network and three-dimensional architecture were largely retained, with only localized fibril aggregation and reduced roughness (~ 28-30 μm). EDS confirmed high chemical purity in scCO<sub>2</sub>-dried aerogels, while minor inorganic traces detected in cryogels were attributed to residual components from the tea-based culture medium. Although scCO<sub>2</sub> drying provided slightly superior structural preservation and textural properties, the porous architecture remained comparable between the two methods. Overall, additive-free BC pellicles produced by static cultivation and processed via limited pre-freezing followed by lyophilization provided a structurally comparable and more sustainable alternative, offering a practical balance between textural performance and processing simplicity. These findings underscore the potential of simplified drying strategies for the sustainable fabrication of BC-based porous materials without compromising structural functionality. . Bacterial cellulose ; Porosity ; Aerogel ; Specific surface area ; Supercritical drying ; Surface roughness ; BET theory ; Nanocellulose ; Supercritical carbon dioxide ; Supercritical fluid Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Polymer Foaming and Composites
Authors: Aradhana Dwivedi | Jan Dellith | Anna A. Makarova | Samir F. El‐Mashtoly | Juergen Popp | Владимир Сиваков | Dana Cialla‐May
Source: Advanced Science (article, 2026-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sulfate‐Directed Silver Dendrites with Enhanced Stability for Ultrasensitive SERS‐Based Therapeutic Drug Monitoring Silver-based surface-enhanced Raman spectroscopy (SERS) substrates offer exceptional signal enhancement but suffer from poor long-term stability due to oxidative degradation, limiting their utility for real-world applications. Here, we introduce sulfate-directed silver dendrites (s-AgDs) as oxidation-resistant SERS substrates with extended shelf life, reproducible signal performance, and sub-picomolar sensitivity. Sulfate ions promote anisotropic growth and form a surface-associated sulfate layer, resulting in dendritic morphologies with high hot-spot density and suppressed surface oxidation. The AgDs demonstrate robust SERS performance over seven months under ambient conditions. We demonstrate translational utility by detecting five clinically relevant drugs in aqueous and blood plasma matrices with minimal pretreatment and excellent reproducibility. These results establish s-AgDs as a stable, reproducible, and application-ready SERS platform for point-of-care therapeutic drug monitoring. Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Aqueous solution ; Limiting ; Therapeutic drug monitoring ; Drug ; Drug detection Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Wissam A. Abed | Ahmed Abdullah | Wasan Kadhim Saad | Jamal Saad
Source: Wasit Journal of Engineering Sciences (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Intelligence-Based Inverse Design of Plasmonic Structures: A Comparative Review of Conventional and Data-Driven Methods This study reviews traditional simulation-based methods and artificial intelligence (AI) approaches for the inverse design of plasmonic structures. Conventional techniques such as the Finite Element Method (FEM), Finite-Difference Time-Domain (FDTD), and Beam Propagation Method (BPM) provide accurate electromagnetic predictions but are computationally demanding, especially in large multi-dimensional design spaces. AI-driven approaches, including machine learning, deep learning, and generative models like Generative Adversarial Networks (GANs) and Conditional Tabular GANs (CTGANs), offer faster predictions of structural parameters from optical targets and enable synthetic dataset generation to address data scarcity. The analysis outlines the strengths and limitations of both strategies, emphasizing their complementary role in advancing high-performance plasmonic devices. Particular focus is placed on their importance for sixth-generation (6G) communication systems, which require high-speed, energy-efficient, and densely integrated optical hardware Focus (optics) ; Plasmon ; Generative grammar ; Inverse problem ; Inverse ; Finite element method ; Deep learning ; Artificial neural network Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing
Authors: Tuula Tenkanen | A. Ylinen | Paula Jouhten | Merja Penttilä | Sandra Castillo
Source: PLoS Computational Biology (article, 2026-03-19)
Field: Materials Science — Biomaterials

PHA synthase variant design using a conditional variational autoencoder Polyhydroxyalkanoate (PHA) synthases are a group of complex, dimeric enzymes which catalyze polymerization of R-hydroxyacids into PHAs. PHA properties depend on their monomer composition but enzymes found in nature have limited specificities to certain R-hydroxyacids only. In this study, a conditional variational autoencoder was used for the first time to design novel PHA synthases. The model was trained with native protein sequences obtained from Uniprot and was used for the creation of approximately 10 000 new PHA synthase enzymes. Out of these, 16 sequences were selected for in vivo validation. The selection criteria included the presence of conserved residues such as catalytic amino acids and amino acids in the dimer interface and structural features like the number of [Formula: see text]-helices in the N-terminal part of the enzyme. Two of the 16 novel PHA synthases that had substantial numbers of amino acid substitutions (87 and 98) with respect to the most similar native enzymes were confirmed active and produced poly(hydroxybutyrate) (PHB) when expressed in yeast S. cerevisiae. The results show the power of AI based methods to create active variants of highly complex dimer enzymes. Amino acid ; Autoencoder ; Enzyme ; Dimer ; Yeast ; Protein engineering ; ATP synthase ; Isozyme biodegradable polymer synthesis and properties ; Microbial metabolism and enzyme function ; Microplastics and Plastic Pollution
Authors: Shuang Liang | Xuliang Deng | Xinyu Liu
Source: Nano Research (article, 2026-02-09)
Field: Chemistry — Organic Chemistry

Long-circulating protein-polymer conjugates: Advancing beyond PEGylated proteins Protein-polymer conjugation has emerged as a powerful approach to prolong the in vivo half-life of therapeutic proteins, enabling reduced administration frequency and improved patient compliance. Among various conjugation methods, PEGylation has become the most successful strategy for more than 34 clinically approved drugs and have established the gold standard for long-acting biotherapeutics. Over the past decades, continuous advances in polymer chemistry and conjugation technologies have driven the innovation of alternatives to PEG to overcome its inherent limitations, such as non-biodegradability, limited functionality, and potential immunogenicity. In this review, we summarize the development and recent progress of long-circulating protein-polymer conjugates beyond PEGylation, focusing on emerging PEG alternatives, key design principles such as size expansion, shape modulation, controlled release, active targeting, and immunomodulation. Furthermore, the current challenges in large-scale manufacture, quality control, and clinical translation are discussed. This review provides insights into the emerging directions for next-generation long-circulating conjugates and charts a rational path toward the design of more effective long-acting therapeutics. PEGylation ; Rational design ; Translation (biology) ; Pharmaceutical technology ; Quality by Design ; Clinical Practice Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications ; Polymer Surface Interaction Studies
Authors: Ryotaro Koga | Takatoshi Kinoshita | M Ishiguro | Momoko Fujita | Hitoshi Chatani | Hikaru KATAOKA | Hideshi Yokoyama | Takehisa Hanawa | Isao Shitanda | Satoru Goto
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Chemistry — Electrochemistry

Hydrophobic hydration of analgesics and diltiazem complexes explored by electrochemical impedance spectroscopy and diffusion-ordered spectroscopy In the context of drug-drug interactions, the combination of benzodiazepine (BDZ) or benzothiazepine (BTZ) antidepressant/hypnotics with commonly used analgesics, including over-the-counter nonsteroidal anti-inflammatory drugs (NSAIDs), has been reported to decrease the effectiveness of analgesics. This study aimed to analyze the diffusion behavior of analgesics and diltiazem (DTZ), which was used as a structural model of BDZ/BTZ, in solution using NMR diffusion-ordered spectroscopy (DOSY) and electrochemical impedance spectroscopy (EIS). Analgesics and DTZ were dispersed as free entities in deuterated dimethyl sulfoxide (an aprotic solvent), whereas oligomeric clathrates were observed in heavy water (a protic solvent). These findings suggest that the hydrophobic hydration of the complex formed by acidic and basic drugs may involve intermolecular electrostatic interactions embedded within the hydrogen-bonding network in water. Dielectric spectroscopy ; Context (archaeology) ; Electrochemistry ; Spectroscopy ; Nuclear magnetic resonance spectroscopy ; Intermolecular force ; Infrared spectroscopy ; Dimethyl sulfoxide Electrochemical Analysis and Applications ; NMR spectroscopy and applications ; Radioactive element chemistry and processing