jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tubanur Avci | Aslıhan Çalhan | Eda  Nur Ayar-Sumer | Canan Ekinci Doğan | Hamid Kaddami | Oguzhan Gunduz | Roger J. Narayan
Source: Sustainable Food Technology (article, 2026-01-01)
Field: Materials Science — Biomaterials

Sustainable and eco-friendly bacterial cellulose production using agro-forest waste for food packaging applications: a review Bacterial cellulose (BC) is a biopolymer with several unique properties, including high purity, crystallinity, biocompatibility, biodegradability, and excellent mechanical strength. Bacterial cellulose ; Food packaging ; Biopolymer ; Cellulose ; Production (economics) ; Food processing ; Food waste Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites
Authors: Mohanned J. Al-Anber | R. Hameed
Source: DYNA (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Theoretical investigation of Hydrogen sulfide adsorption on doped phthalocyanine nanosheets: DFT calculations Density functional theory (DFT) computations of phthalocyanine (MePc) monolayers. Utilizing the B3LYP functional alongside a 6-311+G(d,p) basis set, we conducted comprehensive optimization of the molecular structures to evaluate their stability and electrical characteristics. The binding energy between the central metal and the phthalocyanine monolayer was computed, together with the adsorption energy of H₂S, to assess the feasibility for gas sensing applications. Our results demonstrate that the adsorption process is primarily spontaneous, with considerable charge transfer occurring during adsorption, as confirmed by Hirshfeld charge calculations. Furthermore, we examined the band-gap energy, global hardness, and electrophilicity index to further the characterization of the sensing capabilities of both pristine and metal-doped phthalocyanine structures. The findings indicate that metal doping improves the sensitivity and stability of phthalocyanine monolayers, making them viable candidates for H₂S detection across diverse applications. This study examines the adsorption characteristics of hydrogen sulfide (H₂S) on metal-doped materials. Phthalocyanine ; Adsorption ; Density functional theory ; Monolayer ; Doping ; Hydrogen sulfide ; Metal ; Hydrogen Porphyrin and Phthalocyanine Chemistry ; Industrial Gas Emission Control ; Molecular Junctions and Nanostructures
Authors: Dina Abdelmguid | Michael Kloster | Bánk Beszteri
Source: Biodiversity and Conservation (article, 2026-01-01)
Field: Materials Science — Biomaterials

Retrieving fine-grained diatom classifications from deep learning models trained on coarse-grained data by over-clustering Benthic diatoms, unicellular microalgae, are important bioindicators of freshwater ecosystem health. However, identifying diatoms using light microscopy is very challenging due to the existence of thousands of species, limited human resources, and the close morphological similarity between species. Automating this process using machine learning could enhance biodiversity monitoring; however, the scarcity of training data across species limits its effectiveness. For biodiversity monitoring and ecological status assessment, species-level identifications are preferred. Genus-level identifications are a first approach until training data covering a sufficient number of species is available. Moreover, models trained on genus-level labels might still learn to separate species in their feature space. We refer to the application of a clustering method to representations learned by models trained on a coarse (genus-level) classification task, with the aim of resolving clusters at a finer granularity (species), over clustering. This study evaluates the over-clustering capabilities of different deep learning architectures: a convolutional neural network (CNN), a vision transformer, and MAPLE, a CNN-based model incorporating X-means clustering during training. For CNN and vision transformer, features were clustered using HDBSCAN or X-means; for MAPLE, we used its internal clustering. Comparing the performance of the models by visual ordinations and clustering metrics, we found that although none of the models come close to separating all species within a genus, according to most metrics, MAPLE can most effectively separate species in the latent space. We suggest using over-clustering on pre-clustered novel diatom images to speed up expert annotation at the species level. This will contribute to an efficient collection of training data with broad species coverage. Cluster analysis ; Deep learning ; Convolutional neural network ; Feature (linguistics) ; Artificial neural network ; Similarity (geometry) ; Biodiversity ; Annotation ; Diatom Diatoms and Algae Research ; Species Distribution and Climate Change ; Water Quality Monitoring Technologies
Authors: Jingmin Liu | Danya Shen | Wen Zhao | Hongyu Zhang | Maohua Rong | Kaige Wang | Ligang Zhang | Libin Liu
Source: Materials (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Study on Main Diffusion Coefficients and Atomic Mobility of Alloying Elements in the β-Phase of the Ti–Zr–Ta Ternary System Diffusion-controlled processes exert an indispensable influence on the thermal processing and microstructural homogenization of <i>β</i>-titanium alloys containing multiple <i>β</i>-stabilizing elements. However, credible multicomponent diffusion kinetic data corresponding to the <i>β</i>-phase within the Ti-Zr-Ta ternary system remain inadequate. In this work, diffusion characteristics within the <i>β</i> single-phase domain of the Ti-Zr-Ta system were investigated using solid-state diffusion couples combined with a numerical inverse method. Twelve diffusion couples in total were synthesized and subjected to annealing treatments at 1373, 1423, and 1473 K, with the corresponding composition-distance distributions quantified by electron probe microanalysis (EPMA). The composition-dependent main interdiffusion coefficients were measured via the numerical inverse method embedded in the HitDIC computational platform, while the atomic mobility parameters corresponding to the <i>β</i>-phase were refined to replicate the experimental concentration distributions and diffusion trajectories across the studied temperature and composition intervals. The results reveal pronounced temperature and composition dependence of the main interdiffusion coefficients, and the diffusion rate of Zr is faster than that of Ta in the <i>β</i> phase. Ternary operation ; Diffusion ; Inverse ; Homogenization (climate) ; Electron probe microanalysis ; Annealing (glass) ; Kinetic energy ; Analytical Chemistry (journal) Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties ; Orthopaedic implants and arthroplasty
Authors: Sadjad Baradari | Natalia Resnina | Mahmoud Nili-Ahmadabadi
Source: Discover Mechanical Engineering (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Effect of aging on the martensitic transformation and functional properties of a Ni44.8Ti45.2Hf5Cu5 multi component shape memory alloy In this study, a Ni44.8Ti45.2Hf5Cu5 shape memory alloy was fabricated by vacuum arc-melting in a copper mold. The alloy was solution annealed at 1000 °C for 60 min and subsequently aged at a temperature range between 300 °C and 600 °C for different time intervals from 10 to 240 min. The alloy underwent a B2 ↔ B19′ one-stage martensitic transformation. The results showed that martensitic transformation temperatures increased with increasing aging temperature from 300 to 600 °C. Increasing transformation temperatures with aging temperature is considered to be due to the suppression of Ti2Ni-type precipitates at higher aging temperatures. Constant stress thermal cycling experiments were carried out under a tensile stress of 200 MPa. The results indicated that aging treatment at a proper temperature could improve the recoverable strain in the alloy. The solution-annealed Ni44.8Cu5Ti45.2Hf5 alloy showed a recoverable strain of $$\sim $$ 6%, while after aging at 300 °C for 60 min, a recoverable strain as high as $$\sim $$ 8% was obtained under 200 MPa. On the other hand, the irrecoverable strain in the alloy was not affected by aging treatment and remained almost constant at close to 1% for all conditions. Furthermore, two-way shape memory effect in Ni44.8Ti45.2Hf5Cu5 alloy was developed by tensile deformation of martensite with a training strain of 3%. After solution annealing, the alloy exhibited a two-way shape-memory strain of $$\sim $$ 1.8%. With increasing aging temperature, the two-way shape memory strain increases until it reaches a maximum value of $$\sim $$ 2.2% at 400 °C and then decreases with further increasing the aging temperature. Shape-memory alloy ; Alloy ; Diffusionless transformation ; Martensite ; Temperature cycling ; Strain (injury) ; Deformation (meteorology) Shape Memory Alloy Transformations ; Metallic Glasses and Amorphous Alloys ; Metallurgical and Alloy Processes
Authors: Wenlong Mu | Liangyu Chen | Jun Jiang | Shufen Zhang | Jingxin Na
Source: Polymer Composites (article, 2026-02-10)
Field: Materials Science — Polymers and Plastics

Hydrothermal Aging Behavior and Mechanical Properties Degradation of Flax/Basalt Fiber Hybrid Composites ABSTRACT To enhance the durability of flax fiber reinforced composites and promote their broader industrial applications, hydrophobic basalt fiber was incorporated to prepare flax/basalt fiber hybrid reinforced composites (HFRP). Tensile, three‐point bending, low‐velocity impact, and post‐impact bending tests were performed on HFRP laminates with different stacking sequences after different aging cycles. Combined with Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectroscopy (FTIR) characterization, the failure mechanism of HFRP in a hydrothermal aging environment was analyzed. The results indicate that the moisture absorption behavior of HFRP at different temperatures follows Fickian law. When basalt fiber is positioned in the outer layer, the material's moisture absorption rate and saturated moisture absorption rate are effectively reduced (the maximum decline was approximately 66%). Tensile and there‐point bending strength of composites decrease with higher hydrothermal aging temperatures across all layups. Among them, the performance of [B 2 F 2 ] S and [F 2 B 2 ] S deteriorated more significantly. The tensile strength decreased by 67.44% and 54.7% compared to the initial strength, while the bending strength decreased by 46.13% and 49.78%. The impact resistance and there‐point bending properties after impact of hybrid composites before and after aging are improved compared with single fiber composites. The there‐point bending strength retention rate (63% ~ 82%) of [F 4 B 4 ] S under different aging conditions is higher than that of [F 16 ] (57% ~ 77%), and the there‐point bending strength retention rate (51% ~ 74%) of [B 4 F 4 ] S is higher than that of [B 16 ] (48% ~ 72%). This study reveals the performance degradation mechanism of HFRP in extreme environments and provides a theoretical basis for optimizing material design and improving its application performance in complex environments. Basalt fiber ; Ultimate tensile strength ; Bending ; Fiber ; Hydrothermal circulation ; Moisture ; Flexural strength ; Differential scanning calorimetry Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Bamboo properties and applications
Authors: Ethan B.T. Rogers | Sai Srikanth Lakkimsetty | Kylie Ariel Bemis | C. A. Schurman | Peggi M. Angel | Birgit Schilling | Olga Vitek
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-04-10)
Field: Chemistry — Spectroscopy

Statistical Principles Define an Open-Source Differential Analysis Workflow for Mass Spectrometry Imaging Experiments with Complex Designs Mass spectrometry imaging (MSI) characterizes the spatial heterogeneity of molecular abundances in biological samples. Experiments with complex designs, involving multiple conditions and multiple samples, provide particularly useful insight into differential abundance of analytes. However, analyses of these experiments require attention to details such as signal processing, selection of regions of interest, and statistical methodology. This manuscript contributes a statistical analysis workflow for detecting differentially abundant analytes in MSI experiments with complex designs. Using a case study of histologic samples of human tibial plateaus from knees of osteoarthritis patients and cadaveric controls, as well as simulated datasets, we illustrate the impact of the analysis decisions. We illustrate the importance of signal processing and feature aggregation for preserving biological relevance and alleviating the stringency of multiple testing. We further demonstrate the importance of selecting regions of interest in ways that are compatible with differential analysis. Finally, we contrast several common statistical models for differential analysis, showcase the appropriate use of replication, and demonstrate model-based calculation of sample size for followup investigations. The discussion is accompanied by detailed recommendations and an open-source R-based implementation that can be followed by other investigations. Workflow ; Sample size determination ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Differential (mechanical device) ; Statistical analysis ; Sample (material) ; Relevance (law) ; Signal processing ; Feature (linguistics) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Ion-surface interactions and analysis
Authors: Zimu Zhou | Enrique A. López‐Guerra | I. Zana | Vu Nguyen | Nguyen Quoc Huy Tran | Violet Huang | Bin Zhou | Gary Qian | Michael Kwan | Peter Wilkens | Chester Chien
Source: Metrology (article, 2026-02-19)
Field: Materials Science — Surfaces, Coatings and Films

Extending Reflectometry Range: A Zero-Crossing Algorithm for Thick Film Metrology Accurate and high-efficiency film metrology remains a key challenge in High-Volume Manufacturing (HVM), where conventional spectroscopic reflectometry and white light interferometry (WLI) are either limited by model dependence or throughput. In this work, we extend the measurable film-thickness range of reflectometry to at least 50 µm through a new model-free algorithm, the Linearized Reflectance Zero-Crossing (LRZ) method. The approach builds upon the previously reported Linearized Reflectance Extrema (LRE) technique but eliminates the sensitivity to spectral sampling and fringe attenuation that degrade performance in the thick-film regime. By linearizing phase response and extracting Zero-Crossing positions in wavenumber space, LRZ provides robust and repeatable thickness estimation without iterative fitting, achieving comparable accuracy with much higher computational efficiency than conventional model-based methods. Validation using more than 80 measurements on alumina films over NiFe substrates shows excellent correlation with WLI (r = 0.97) and low gauge repeatability and reproducibility (GR&amp;R &lt; 3%). Moreover, LRZ achieves an average Move-Acquire-Measure (MAM) time of approximately 2 s, which is about 7 times faster than WLI. The proposed method enables fast, accurate, and model-independent optical metrology for thick films, offering a practical solution for advanced HVM process control. Reflectometry ; Metrology ; Repeatability ; Interferometry ; Reproducibility ; Attenuation ; White light interferometry ; Calibration Optical Coatings and Gratings ; Optical measurement and interference techniques ; Optical Polarization and Ellipsometry
Authors: Yun-Hsuan Chen | Jen-Yu Chang | Tsung‐Wei Huang | En-Jui Kuo
Source: ArXiv.org (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Meta-Learning for GPU-Accelerated Quantum Many-Body Problems We explore the industrial and scientific applicability of the VQE-LSTM framework by integrating meta-learning with GPU accelerated quantum simulation using NVIDIA's CUDA-Q (CUDAQ) platform. This work demonstrates how an LSTM-FC meta-initialization module can extend the practical reach of the Variational Quantum Eigensolver (VQE) in both chemistry and physics domains. In the chemical regime, the framework predicts ground-state energies of molecular Hamiltonians derived from PySCF, achieving near FCI accuracy while maintaining favorable O(N^2) scaling with molecular size. In the physical counterpart, we applied the same model to quantized Simple Harmonic Motion systems (SHM), successfully reproducing its ground and excited states through VQE and Variational Quantum Deflation (VQD) methods. Benchmark results on NVIDIA GPUs reveal significant speedups over CPU-based implementations, validating CUDAQ's capability to handle large-scale variational workloads efficiently. Overall, this study establishes VQE-LSTM as a viable and scalable approach for GPU accelerated quantum simulation, bridging quantum chemistry and condensed-matter physics through a unified, meta-learned initialization strategy. Quantum ; Initialization ; Scaling ; Quantum algorithm ; Benchmark (surveying) ; Quantum chemistry ; Excited state ; Quantum simulator ; Scalability Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Jun Hyeong Kim | Kyunghoon Lee | Hyeonsu Kim | MinSoo Kang | Suk-Ku Chang | Yinglan Jin | Dongwook Kim | Woo Youn Kim
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Harnessing generative AI for efficient organic materials discovery in low-data regimes A building block-based generative AI combined with DFT screening enables the efficient discovery of TADF materials in low-data regimes. Experimental validation confirms the design of green emitters with external quantum efficiencies up to 11.22%. Generative grammar ; Generative model ; Quantum ; Generative Design Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: L. Ghiringhelli | A. Zambon | G. Tiana
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We find that, at variance with feedforward networks, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties. We show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Finally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning. Embedding ; Feed forward ; Transformer ; Sampling (signal processing) ; Training set ; Dimension (graph theory) ; Langevin dynamics ; Sequence (biology) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Generative Adversarial Networks and Image Synthesis
Authors: Abhishek Tiwari
Source: ChemRxiv (article, 2026-02-16)
Field: Chemistry — Physical and Theoretical Chemistry

HBAT 2: A Python Package to Analyse Hydrogen Bonds and Other Non-covalent Interactions in Macromolecular Structures Hydrogen bonds and other non-covalent interactions play a crucial role in maintaining the structural integrity and functionality of biological macromolecules such as proteins and nucleic acids.Accurate identification and analysis of these interactions are essential for understanding molecular recognition, protein folding, and drug design.HBAT (Hydrogen Bond Analysis Tool) is software for analysing hydrogen bonds and other weak interactions in macromolecular structures.This paper presents HBAT 2, an updated Python reimplementation of the original HBAT tool published in 2007.HBAT 2 is a Python package for automated analysis of hydrogen bonds and other non-covalent interactions in macromolecular structures available in Protein Data Bank (PDB) file format.The software identifies and analyses traditional hydrogen bonds, weak hydrogen bonds, halogen bonds, X-H#, #-# stacking, and n##* interactions using geometric criteria.It also detects cooperativity and anticooperativity chains and renders them as 2D visualisations.The latest version offers improved cross-platform tkinter-based graphical user interface (GUI), a web-based interface, a simple command-line interface (CLI), and a developer-friendly API, making it accessible to users with different computational backgrounds. Hydrogen bond ; Macromolecule ; Python (programming language) ; Hydrogen Crystallography and molecular interactions ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Yuanzhe Feng | Ying Zhao | Jiaxin Han | Fenghaowen Li | Gong Li | Bin Wang | Qing Chen | Jibin Song
Source: Nano Research (article, 2026-02-01)
Field: Materials Science — Biomaterials

From mechanisms to applications: Research advances and translational prospects of endogenous targeting LNPs Selective Organ-Targeting (SORT) LNPs have emerged as a transformative platform in precision medicine, enabling efficient and safe delivery of therapeutic agents to specific organs. This review explores the mechanistic foundations, design strategies, and biomedical applications of SORT LNPs. Key targeting mechanisms include dynamic protein corona formation, which mediates tissue-specific uptake through endogenous interactions, and the rational modulation of nanoparticle size, surface chemistry, and material composition to manipulate in vivo biodistribution. Recent advancements highlight targeted delivery systems for the liver, lungs, and spleen, leveraging both passive and active targeting as well as endogenous cues. Despite the clinical success of mRNA and siRNA therapies, challenges such as immunogenicity, biodistribution complexity, and interspecies translation remain. This review provides a comprehensive overview of SORT development, offering insights into the next generation of intelligent, biocompatible, and organ-specific nanomedicines for cancer and immune-related diseases. Endogeny ; Rational design ; Transformative learning ; Nanomedicine ; sort ; Biodistribution Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics