jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chihab Ezzaki | Anas Chaari | Amani Al–Othman
Source: Polymers (review, 2025-11-18)
Field: Materials Science — Biomaterials

Recent Advances on Chitosan-Based Nanoparticles for Brain Drug Delivery The blood-brain barrier (BBB) represents a major challenge in effective drug delivery systems intended for treating neurological disorders. It restricts the transport of therapeutic agents to the brain. Chitosan-based nanoparticles (CNPs) can be used for brain drug delivery because of their biocompatibility, biodegradability, and ability to enhance drug permeability across the BBB. This review article discusses the design and application of CNPs for brain-targeted drug delivery, exploring their mechanisms of action, including adsorptive-mediated and receptor-mediated endocytosis. Surface modifications with ligands such as chlorotoxin are discussed for improving specificity and therapeutic results. Findings show that CNPs allow controlled drug release, enhance stability, and reduce side effects, which make them effective for treating multiple neurological conditions, including Alzheimer's disease, Parkinson's disease, brain tumors, and ischemic stroke. CNPs can encapsulate multiple therapeutic agents, such as anti-inflammatory drugs, cytotoxic agents, and genetic materials, and maintain stability under different physiological conditions. Intranasal delivery routes are mainly discussed in this paper for their ability to bypass systemic circulation and achieve direct brain targeting. This review also addresses challenges such as cytotoxicity and the need for optimizing nanoparticle size, charge, and surface properties to improve the therapy results. While CNPs are suitable for brain drug delivery, there is a research gap, which is the lack of systematic studies evaluating their long-term effects on brain tissue and health. Most studies focus on acute therapeutic outcomes and in vitro or short-term in vivo analysis, which do not address some questions about the chronic exposure risks, biodistribution, and clearance pathways of CNPs. This review also explores the use of chitosan-based nanoparticles to deliver drugs to the brain for the treatment of multiple neurological disorders. Drug delivery ; Drug ; In vivo ; Blood–brain barrier ; Nasal administration ; Targeted drug delivery ; Systemic circulation Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Barrier Structure and Function Studies
Authors: Wenzhi Ma | Zhenyu Liu | Jincai Yang | Changchun Lu | Hanyu Zhang | Wenbo Xiao
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-22)
Field: Materials Science — Materials Chemistry

From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeter Abstract Recent advances in deep learning have significantly improved the accuracy of structure prediction for biomolecular complexes; however, robust evaluation of these models remains a major challenge. We introduce PXMeter , an open-source toolkit that support consistent and reproducible evaluation of diverse predictive models across a broad spectrum of biological complex structures. PXMeter provides a unified and reproducible benchmarking framework, offering valuable insights to support the ongoing improvement of structure prediction methods. We also present a high-quality benchmark dataset curated from recently deposited structures in the Protein Data Bank (PDB). These entries are manually reviewed to exclude non-biological interactions, ensuring reliable evaluation. Using these resources, we conducted a comprehensive benchmark of several structure prediction models, namely Chai-1, Boltz-1, and Protenix. Our benchmarking results demonstrate the advancements achieved by deep learning models, while also identifying ongoing challenges—especially in modeling protein-protein and protein-RNA interactions. Project Page https://github.com/bytedance/PXMeter Computer science ; Data curation ; Data science Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Xiaomin Jia | Chang Tong | Shihui Zhou | Xiaolong Zhang | Shupeng Wang | Zhihui Zhang
Source: Journal of Bionic Engineering (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Progress in Bionic Deformable Wing of Aircraft Driven by Shape Memory Alloy Abstract Birds have developed near-perfect structures and functionality over millions of years of natural evolution. To improve the efficiency of fixed-wing vehicles in different environments, researchers have developed deformable wings inspired by the wing structures of birds. Shape Memory Alloy (SMA) is applied as a smart material to the deformable wing. Compared with other drive methods, SMA actuators have the advantages of high drive capacity and a simple structure for driving wing deformation. According to the shape memory effect, SMA actuators are classified as single-range and dual-range actuators. The wing structure designed for each SMA drive is unique. By comparing and analyzing the structures of airfoils, airfoils with similar drive forms and deformation structures are put together for review and discussion. The deformable wings are categorized into out-of-face deformation, in-face deformation, airfoil curvature deformation, and combined deformation with multiple degrees of freedom based on the structure and location of the wing that produces the deformation. An overview of the deformed wing is introduced by telling the bionic theory of seagulls. The principles of deformation of the wing, the mechanics of the SMA actuator mechanism, and the aerodynamic characteristics of the deformable wing are presented. The structure and working principle of SMA actuators for each type of deformable wing are explained in detail. Methods and approaches to study the deformability of deformable wings are analyzed and summarized. This work provides comprehensive insights and perspectives for future studies of SMA-driven deformable airfoils. Wing ; Shape-memory alloy ; Alloy ; Materials science ; Aerospace engineering ; Computer science ; Engineering ; Composite material Shape Memory Alloy Transformations ; Biomimetic flight and propulsion mechanisms ; Aeroelasticity and Vibration Control
Authors: Jannik Rothkegel | Sylvia Kaufmann | Michaela Wilsch‐Bräuninger | Catarina Lopes | Rita Mateus
Source: Small Methods (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Purine Molecular Interactions Determine Anisotropic Shape of Zebrafish Biogenic Crystals Across phyla, many organisms self-organize crystals, for functions like vision, pigmentation, and metabolite storage. In zebrafish, a vertebrate known for its crystal-based color patterns, iridophores concentrate purines in membrane-bound organelles, the iridosomes. Inside these vesicles, crystals assemble into large, flat, and thin hexagons following unknown mechanisms that defy typical thermodynamic interactions. Here, we investigate the development of zebrafish iridosomal crystals by using live imaging, cryoFIB-SEM, and novel morphometric analysis pipelines. In doing so, we find that crystal growth predominantly occurs along the b-crystallographic axis, producing their characteristic anisotropic shape. By performing comparative genetic analyses in vivo and reproducing such conditions in silico, we uncover that the zebrafish crystals' in-plane hydrogen bond molecular structure is the main determinant for the observed crystal anisotropy. Macroscopically, the b-axis anisotropy is controlled by the ratio of guanine-to-hypoxanthine in the iridosome, without affecting the other axes. At the atomic level, the extent of the (100) facet anisotropy depends entirely on the type, number, and strength of molecular H-bonds within the crystal lattice. Mechanistically, our work shows that purine diversity and availability inside the zebrafish iridosome is key to form an anisotropic crystal lattice, leading to the observed functional crystal shapes. Zebrafish ; Anisotropy ; Crystallography ; Crystal (programming language) ; Chemical physics ; Crystal structure ; Materials science ; Biophysics ; Hydrogen bond ; Chemistry ; Molecule ; Biology ; Biochemistry ; Physics ; Optics ; Gene Enzyme Structure and Function ; Erythrocyte Function and Pathophysiology ; Photosynthetic Processes and Mechanisms
Authors: Chanaprom Cholsuk | Sujin Suwanna | Tobias Vogl
Source: Journal of Materials Chemistry C (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Advancing the hBN defects database through photophysical characterization of bulk hBN Overview of the h-bn.info database and the workflow of its generation. Characterization (materials science) ; Workflow ; Work (physics) ; Biocompatible material Diamond and Carbon-based Materials Research ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Guifu Ma | Seyed Mohamad Javidan | Yiannis Ampatzidis | Zhao Zhang
Source: Sensors (article, 2025-07-09)
Field: Chemistry — Analytical Chemistry

A Novel Hybrid Technique for Detecting and Classifying Hyperspectral Images of Tomato Fungal Diseases Based on Deep Feature Extraction and Manhattan Distance Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the integration of few-shot learning with hyperspectral imaging to detect four major fungal diseases in tomato plants: <i>Alternaria alternata</i>, <i>Alternaria solani</i>, <i>Botrytis cinerea</i>, and <i>Fusarium oxysporum</i>. Following inoculation, hyperspectral images were captured every other day from Day 1 to Day 7 post inoculation. The proposed hybrid method includes three main steps: (1) preprocessing of hyperspectral image cubes, (2) deep feature extraction using the EfficientNet model, and (3) classification using Manhattan distance within a few-shot learning framework. This combination leverages the strengths of both spectral imaging and deep learning for robust detection with minimal data. The few-shot learning approach achieved high detection accuracies of 85.73%, 80.05%, 90.33%, and 82.09% for <i>A. alternata</i>, <i>A. solani</i>, <i>B. cinerea</i>, and <i>F. oxysporum</i>, respectively, based on data collected on Day 7 post inoculation using only three training images per class. Accuracy improved over time, reflecting the progressive nature of symptom development and the model's adaptability with limited data. Notably, <i>A. alternata</i> and <i>B. cinerea</i> were reliably detected by Day 3, while <i>A. solani</i> and <i>F. oxysporum</i> reached dependable detection levels by Day 5. Routine visual assessments showed that <i>A. alternata</i> and <i>B. cinerea</i> developed visible symptoms by Day 5, whereas <i>A. solani</i> and <i>F. oxysporum</i> remained asymptomatic until Day 7. The model's ability to detect infections up to two days before visual symptoms emerged highlights its value for pre-symptomatic diagnosis. These findings support the use of few-shot learning and hyperspectral imaging for early, accurate disease detection, offering a practical solution for precision agriculture and timely intervention. Alternaria alternata ; Hyperspectral imaging ; Botrytis cinerea ; Artificial intelligence ; Fusarium oxysporum ; Feature extraction ; Alternaria solani ; Computer science ; Preprocessor ; Septoria ; Pattern recognition (psychology) ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Plant Disease Management Techniques
Authors: Mohamad Yusuf Salim | M. R. M. Asyraf | Abu Hassan Nordin | M.N.N. Harjar | Mohammed Sanal B | K.Z. Hafila | Quan Ma | A.I. Ra’eis | Mohamad Ridzwan Ishak | Muhammad Rizal Razman | Zuliskandar Ramli | Tabrej Khan | Tamer A. Sebaey
Source: Journal of Materials Research and Technology (article, 2025-10-28)
Field: Materials Science — Polymers and Plastics

Natural fibre-reinforced thermoplastic composites: A bibliometric analysis and review of eco-friendly solutions in exterior automotive components The automotive industries are consistently working towards the advancement of lightweight components to enhance fuel efficiency. Thermoplastic matrix composites have been found to provide several notable benefits, including potential weight reduction, recyclability, specific strength, corrosion resistance, cost-efficiency, and design versatility. The variety of material forms and combinations in fibre-reinforced thermoplastic polymers has expanded, opening new possibilities for their use in the automotive sector and beyond. Natural fibres, due to their biodegradability, widespread availability in nature, and cost-effectiveness, offer an opportunity to broaden the range of materials used while reducing reliance on petroleum-based products. Moreover, various modifications are employed to enhance the properties of natural fibres, making them more competitive with synthetic fibres for these applications. This review provides a thorough bibliometric and technical overview of the use of natural fibres as reinforcement fibres in the thermoplastic composites. A particular focus is placed on plant fibres and their potential applications as composite reinforcement for automotive applications, especially exterior components. The review also provides an overview of the properties of commonly available thermoplastic matrices and the various composite fabrication techniques. This information can assist in making informed decisions when selecting the most suitable material and process for specific product development. Furthermore, this review examines the mechanical properties of natural fibre reinforced thermoplastic composites and sheds light on the important challenges that must be addressed. Automotive industry ; Thermoplastic ; Thermoplastic composites ; Thermoplastic polymer ; Thermoplastic elastomer ; Composite number ; Process (computing) Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Advanced Cellulose Research Studies
Authors: Caroline Hiefinger | Michela Marcon | Verena Pape | Guillem Casadevall | Ranit Lahmy | Christoph Haag | Julian Nazet | Michael H. Bartl | Astrid Bruckmann | Sílvia Osuna | Burkhard Koenig | Andrea C. Kneuttinger
Source: Angewandte Chemie International Edition (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Expanding the Repertoire of Photoswitchable Unnatural Amino Acids for Enzyme Engineering Photoswitchable unnatural amino acids (psUAAs) play a crucial role in the engineering of light-sensitivity in enzymes, which holds significant promise for diverse applications such as biotherapy and biocatalysis. Besides near-quantitative photoconversion, the success and expediency of a psUAA for a certain application is defined by its interaction potential with the enzyme, its thermal stability and its effective wavelength of irradiation. To establish high versatility in the current repertoire, we have designed and synthesized six psUAAs based on azobenzene, arylazopyrazole, arylazothiazole, hemithioindigo and spiropyran photoswitches. The resulting psUAAs exhibit an enhanced interaction potential within an enzyme owing to their capacity for hydrogen bonding, ionic interactions and metal ion coordination. Moreover, we observed diverse photochemical behaviors among the psUAAs, with four of them reversibly switching between the isomers with purely visible light. Notably, we identified orthogonal aminoacyl-tRNA synthetases that facilitate the incorporation of five of the six psUAAs co-translationally and computationally analyzed the synthetase-psUAA interactions. Finally, we evaluated the photochemical behavior of the five psUAAs within an enzymatic model and tested the photocontrol of catalysis confirming their diversity. Ultimately, our findings significantly expanded the repertoire of psUAAs and demonstrated their feasibility for enzyme engineering studies. Azobenzene ; Enzyme ; Chemistry ; Protein engineering ; Biocatalysis ; Amino acid ; Combinatorial chemistry ; Catalysis ; Ionic liquid ; Biochemistry ; Organic chemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Sangjun Han | Hong Jin Kong | Junho Choi | Won Chegal | Min Seok Jang
Source: Light Science & Applications (article, 2025-08-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-gate electro-optic beam switching metasurfaces Electro-optic active metasurfaces have attracted attention due to their ability to electronically control optical wavefronts with unprecedented spatiotemporal resolutions. In most studies, such devices require gate arrays composed of a large number of independently-controllable local gate electrodes that address the local scattering response of individual metaatoms. Although this approach in principle enables arbitrary wavefront control, the complicated driving mechanism and low optical efficiency have been hindering its practical applications. In this work, we demonstrate an active beam switching device that provides highly directional beam profiles and significant and uniform optical efficiencies across diffraction orders separated by a large deflection angle. The device operates with only a single-gate bias applied to monolayer graphene, modulating its optical conductivity to control the optical efficiency of the device. The key performance metrics, the absolute and the relative efficiency, which are defined as the scattered power toward a certain angle θ normalized by the incident power and the net scattered power from the metasurface, respectively, are maximized by a genetic algorithm. Experimentally, the metasurface achieves 57° of active beam switching from the 0th to the -1st order diffraction, with absolute efficiencies of 0.084 and 0.078 and relative efficiencies of 0.765 and 0.836, respectively. Furthermore, an analytical framework using nonlocal quasinormal mode expansion provides deeper insight into the operating mechanism of active beam switching. Finally, we discuss the performance limitations of this design platform and provide insights into potential improvements. Optics ; Optoelectronics ; Optical switch ; Quantum optics ; Beam (structure) ; Materials science ; Nanophotonics ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic and Optical Devices
Authors: Sebastian Ehlert | Jan Hermann | Thijs Vogels | Victor García Satorras | Stephanie Lanius | Marwin Segler | Derk P. Kooi | Kenji Takeda | Chin‐Wei Huang | Giulia Luise | Rianne van den Berg | Paola Gori‐Giorgi | Amir Karton | Karton, Amir
Source: ArXiv.org (preprint, 2025-06-17)
Field: Materials Science — Materials Chemistry

Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space Accurate thermochemical data with sub-chemical accuracy (within 1 kcal mol$^{-1}$ of the empirical ground truth) are essential for advancing computational chemistry methods. However, existing datasets that reach this level of accuracy remain limited in size or scope. This hinders the development of data-driven methods with predictive accuracy across the broad chemical space of closed-shell, neutral molecules. Here we present Microsoft Research Accurate Chemistry Collection (MSR-ACC) and its first release, MSR-ACC/TAE25, comprising 73,040 total atomization energies at the CCSD(T)/CBS level obtained with the W1-F12 thermochemical protocol. The dataset is constructed to exhaustively cover the chemical space of closed-shell, charge-neutral, covalently bound equilibrium molecular structures containing up to 5 non-hydrogen atoms drawn from elements up to argon and lacking significant multireference character. The dataset and its canonical train and validation splits are openly available on Zenodo in the QCSchema format under the CDLA Permissive 2.0 license. This first release of MSR-ACC enables data-driven approaches for developing predictive computational chemistry methods with unprecedented accuracy and scope. Chemical space ; Chemical nomenclature ; Space (punctuation) ; Coupled cluster ; Reference data ; Cluster (spacecraft) ; Argon Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Max Pinheiro | Matheus de Oliveira Bispo | Rafael S. Mattos | Mariana T. do Casal | Bidhan Chandra Garain | Josene M. Toldo | Saikat Mukherjee | Mario Barbatti
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

ULaMDyn: enhancing excited-state dynamics analysis through streamlined unsupervised learning The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations. ULaMDyn integrates seamlessly with the Newton-X platform and employs advanced dimensionality reduction and clustering techniques to uncover hidden patterns in molecular trajectories, enabling a more intuitive understanding of excited-state processes. Using the photochemical dynamics of fulvene as a test case, we demonstrate how ULaMDyn efficiently identifies critical molecular geometries and critical nonadiabatic transitions. The package offers a streamlined, scalable solution for interpreting large NAMD datasets. It is poised to facilitate advances in the study of excited-state dynamics across a wide range of molecular systems. Dimensionality reduction ; Computer science ; Cluster analysis ; Molecular dynamics ; Python (programming language) ; Curse of dimensionality ; Scalability ; Excited state ; Unsupervised learning ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Database Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods