jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ning Song | Zhongfeng Zhang | Wei Li | Guilan Hu | Shaoqing Wang
Source: Research Square (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

Properties of foamed concrete utilizing Fe(II) as foam stabilizer for hydrolyzed pumpkin seed protein Foaming agent ; Stabilizer (aeronautics) ; Hydrolysis ; Shrinkage ; Metal foam ; Metal ions in aqueous solution ; Metal Pickering emulsions and particle stabilization ; Polymer Foaming and Composites ; Bone Tissue Engineering Materials
Authors: Keunyoung Lee | In-Ho Yoon | Junhyuk Jang | Min Ku Jeon | Sung-Wook Kim | Youngho Sihn | Sungjune Sohn | Bum-Kyoung Seo
Source: Economic and Environmental Geology (article, 2026-02-27)
Field: Chemistry — Inorganic Chemistry

Trends in Uranium Resource Development and Pollution Control Technologies for Securing Nuclear Fuel Uranium is an essential raw material for nuclear fuel, and its strategic importance has increased again in response to global carbon neutrality policies and the growing demand for electricity.However, uranium resources are geographically concentrated in a limited number of countries, and environmental contamination associated with uranium mining and milling, including radioactive materials and heavy metals, remains a critical socio-environmental concern.This review comprehensively examines the current status and technological evolution of domestic and international uranium resource development, with a particular focus on pollution generation, control, and remediation technologies.Conventional open-pit and underground mining methods are reviewed alongside in-situ leach mining, which has rapidly increased its share of global uranium production, highlighting their operational principles, advantages, and environmental implications.Key issues in uranium leaching, smelting, and mineral processing are discussed in relation to technical efficiency and environmental risk.Furthermore, representative cases of uranium mine contamination are analyzed to elucidate the geochemical and radiological behavior of uranium in tailings and groundwater systems, and recent advances in disposal design, groundwater restoration, and biogeochemical remediation technologies are summarized.Finally, past and current uranium-related research activities in Korea are reviewed to identify technical limitations and future opportunities, and strategic directions for diversifying uranium supply and achieving environmentally sustainable nuclear fuel security are proposed. Uranium ; Resource (disambiguation) ; Pollution ; Control (management) ; Enriched uranium Radioactive element chemistry and processing ; Radioactive contamination and transfer ; Nuclear and radioactivity studies
Authors: L. Fang | X. Bao | Z. Song | S. Xu | H. Y. Huang
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Reduced-Order Variational Deterministic-Particle-Based Scheme for Fokker-Planck Equations in Microscopic Polymer Dynamics This study proposes an acceleration technique for the computational challenges in extending the variational deterministic-particle-based scheme (VDS) [Bao et al., Journal of Computational Physics 522 (2025) 113589] to 3D complex fluid simulations with multi-bead polymers. While the original VDS effectively captures configuration space dynamics for 2D dumbbell polymers, its direct extensions reveal critical scalability limitations. The growing configuration space dimensionality necessitates prohibitively large particle ensembles to maintain distributional accuracy, so its quadratic computational cost scaling impedes practical applications. In this paper, we develop a model reduction framework integrating proper orthogonal decomposition (POD) to speed up the computation of the VDS for microscopic Fokker-Planck equations. Numerical validation using bead-spring chain models in simple shear flow demonstrates that the computational efficiency of the reduced model increases systematically with molecular complexity. The reduced-order model introduces about $6\%$ relative error in predicting the dynamics while requiring only about $6\%$ of the original computational time for $4$-bead chain polymers, where the relative numerical error of the reference dynamics is about $5\% \sim 10\%$, and the degrees of freedom can be reduced significantly to about $0.1\%$ of the original model, which means the low-dimensional structure is found by POD. This establishes a practical pathway for multiscale and complex fluid simulations. Curse of dimensionality ; Degrees of freedom (physics and chemistry) ; Computation ; Scaling ; Quadratic equation ; Flow (mathematics) ; Scalability ; Computational fluid dynamics ; Computational complexity theory ; Reduction (mathematics) Block Copolymer Self-Assembly ; Model Reduction and Neural Networks ; Rheology and Fluid Dynamics Studies
Source: Advanced mathematical models & applications. (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Mathematical Model of Dispersion Front During Bending and Twisting of Cylindrical Piezoceramics As a result of the theoretical and experimental analysis carried out in the article, a mathematical model has been developed and solved for the problem of the formation and development of the dispersion region during the simultaneous twisting and bending of the tubular cylindrical piezoceramic element of a micro piezoelectric motor, which ensures the movement of the optical mirror used for directing optical signals in FSO (Free-Space Optical Communication).In the general case, curves expressing the motion of the dispersion front have been constructed using numerical computation methods.To calculate the longevity of piezo motors made from tubular cylindrical piezoceramic material operating under mechanical effects of bending and twisting, the influence of forces on the width and height of the cylinder has been analyzed, and the effect of the piezoceramic material on both the stabilization of dispersion and its propagation speed has been studied. Bending ; Dispersion (optics) ; Front (military) ; Piezoelectricity ; Mathematical model Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Composite Material Mechanics
Authors: SiTong Liu | Ruijie Xu | Fanhui Kong | Yuchen Xiao | Yingchao Liu
Source: Academic Journal of Science and Technology (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

CoT-Integrated Lightweight Cross-Modal Model for Scrap Steel Recognition Under the strategic goals of "Dual Carbon," the green transformation of the iron and steel industry hinges on the efficient recycling of scrap steel resources. To address the limitations of existing scrap steel recognition methods—such as low efficiency, reliance on manual experience, insufficient unimodal analysis capability, and deployment challenges—this study proposes a novel cross-modal recognition large model that integrates chain-of-thought (CoT) reasoning and lightweight knowledge distillation. Firstly, a cross-modal recognition framework based on CLIP and SAM is constructed to establish a "shape-image-composition" semantic mapping, enabling fine-grained segmentation and compositional association of scrap steel. Secondly, a model compression strategy incorporating multi-dimensional knowledge transfer and chain-of-thought guidance is designed. This strategy effectively adapts the capabilities of the large model for edge computing devices while preserving high accuracy and ensuring the interpretability of the decision-making process. Finally, an intelligent decision-making closed-loop system of "composition prediction - charge optimization - process calibration" is developed by integrating the aforementioned model. Experimental results demonstrate that the optimized lightweight model achieves an inference speed of 35 FPS on edge devices with a mean Average Precision of 92.1%. System-level simulation shows an 8.2 percentage point increase in scrap steel utilization rate and a significant enhancement in process stability. This research provides a high-precision, high-real-time, and highly reliable solution for the intelligent upgrading of short-process steelmaking. Scrap ; Process (computing) ; Interpretability ; Software deployment ; Workcell ; Enhanced Data Rates for GSM Evolution ; Segmentation Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Metallurgical Processes and Thermodynamics
Authors: Lyle Semple
Source: Open MIND (preprint, 2026-03-26)
Field: Chemistry — Physical and Theoretical Chemistry

Periodic Table Of The Elements; the Prime Number Architecture Each element has unique properties and if a number system was to be used to describe uniqueness, prime numbers stand out. Without uniqueness, the system would be chaotic. Using a numerical system analysis of seven chemically diverse elements (H, He, Ne, Ar, Kr, C, Fe) across 13 physical domains yields 91 and an additional 23 for 104 relationships the author found a prime number numeric pattern. Each reproducing measured NIST values within 0.5% error using rule based parameters, with median error of 0.020%; the maximum of 0.497%. The probability of this arising by coincidence is P ≈ 10⁻⁵oo — but this was reduced as the author found a pattern within the domain. A domain-prime table is established: each physical domain (viscosity, sound speed, ionization energy, thermal conductivity, etc.) possesses a characteristic base prime and denominator, invariant across all elements tested. The first four primes — 2, 3, 5, and 7 — account for all domain operators across all tested elements and properties. Here we demonstrate that the S-field encodes elemental identity through prime-number architecture — specifically, that measurable physical properties of elements are connected by prime-number operators derived exclusively from the prime factorizations of their atomic numbers. NIST ; Prime (order theory) ; Prime number ; Table (database) ; Invariant (physics) ; Coincidence ; Element (criminal law) ; Base (topology) History and advancements in chemistry ; Graph theory and applications ; X-ray Diffraction in Crystallography
Authors: Toshiyuki Kowada | Toshizo Sato | Kento Kasahara | Satoshi Hamaguchi | Konoka Baba | Takato Mashita | Muhammad Wildan Saifudin | Hayashi Yamamoto | Nobuyuki Matubayasi | Shin Mizukami
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Minimal-background chemo-optogenetic dimerization for precise control of cellular signaling Photochromic chemically induced dimerization (CID) system enables reversible optical control of protein–protein interactions in living cells, offering a powerful alternative to conventional optogenetic tools. We previously developed a photochromic CID system using an Escherichia coli dihydrofolate reductase (eDHFR)/HaloTag pair and an azobenzene-based photochromic dimerizer, pcDH, which achieved rapid and reversible protein translocation. However, undesirable background binding resulting from the high affinity of the E-isomer for eDHFR limited its precision. Here, we present an improved system employing an azopyrazole-based dimerizer, pcDH2b, and an engineered eDHFR mutant to reduce background binding. Although pcDH2b retained similar affinity for eDHFR(WT) as pcDH, molecular docking analysis identified F31 as a key residue, prompting cell-based screening of F31 mutants. Among tested variants, eDHFR(F31I) showed minimal background binding while preserving efficient photoreversible dimerization. Fluorescence polarization assays confirmed decreased binding affinity in both dark and violet light-induced photostationary states. The optimized eDHFR(F31I)/Halo–pcDH2b pair exhibited rapid association and dissociation kinetics (t1/2,on = 1.1 ± 0.1 s; t1/2,off < 0.3 s) and enabled precise, low-background optical regulation of mitophagy in HeLa cells. Minimal Parkin recruitment under inactivating green-light illumination suppressed background signaling, whereas violet-light illumination triggered cytPINK1 anchoring to the mitochondrial outer membrane and mitophagy initiation. Molecular dynamics simulations provided insights into the energetic and structural basis of E/Z selectivity. These results demonstrate that tuning ligand-protein affinity through protein mutation effectively minimizes background binding while maintaining fast photo-responses, providing a practical platform for reversible optical control of cellular signaling and post-translational modifications with minimal basal activation. Mutant ; Dihydrofolate reductase ; Fluorescence ; Kinetics ; Optogenetics ; Flash photolysis ; Escherichia coli Photochromic and Fluorescence Chemistry ; Light effects on plants ; Click Chemistry and Applications
Authors: Christian Pehle | Jean-Jacques Slotine
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Riemannian Optimization in Modular Systems Understanding how systems built out of modular components can be jointly optimized is an important problem in biology, engineering, and machine learning. The backpropagation algorithm is one such solution and has been instrumental in the success of neural networks. Despite its empirical success, a strong theoretical understanding of it is lacking. Here, we combine tools from Riemannian geometry, optimal control theory, and theoretical physics to advance this understanding. We make three key contributions: First, we revisit the derivation of backpropagation as a constrained optimization problem and combine it with the insight that Riemannian gradient descent trajectories can be understood as the minimum of an action. Second, we introduce a recursively defined layerwise Riemannian metric that exploits the modular structure of neural networks and can be efficiently computed using the Woodbury matrix identity, avoiding the $O(n^3)$ cost of full metric inversion. Third, we develop a framework of composable ``Riemannian modules'' whose convergence properties can be quantified using nonlinear contraction theory, providing algorithmic stability guarantees of order $O(κ^2 L/(ξμ\sqrt{n}))$ where $κ$ and $L$ are Lipschitz constants, $μ$ is the mass matrix scale, and $ξ$ bounds the condition number. Our layerwise metric approach provides a practical alternative to natural gradient descent. While we focus here on studying neural networks, our approach more generally applies to the study of systems made of modules that are optimized over time, as it occurs in biology during both evolution and development. Lipschitz continuity ; Backpropagation ; Artificial neural network ; Metric (unit) ; Gradient descent ; Modular design ; Riemannian geometry ; Convergence (economics) ; Stability (learning theory) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Reservoir Computing
Authors: Shui Liu | Qi Xie | Yongye Xia | Dun Hu | Jingxia Qiang | Yong Zhang | Bao Zhang | C. C. Zhang | Feng Xu
Source: Advanced Materials (article, 2026-02-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lattice Slot Waveguide for Terahertz Microfluidics Biomedical Trace Analysis Electromagnetic metasurface integrated microfluidic chips enable a real-time, label-free platform for terahertz trace analysis of volume-limited biomedical samples with suppressed water absorption noise. However, conventional metal-insulator-metal (MIM) metasurface resonators exhibit inherently limited Q-factor and sensitivity due to radiative leakage through open side boundaries. Here, a lattice slot waveguide based on MIM configuration is designed to effectively confine energy within the microfluidic channel and mitigate radiative loss. This trapped mode achieves enhanced sensitivity and Q-factor through synergistic excitation of surface lattice resonance and guided mode resonance under propagation constant matching conditions. Leveraging this platform, an anisotropic detection strategy incorporating a patterned lattice structure is devised to achieve simultaneous polarization multiplexed responses, exhibiting a figure of merit of 135 in both polarizations. Experimental validation demonstrates a limit of detection of 625 pmol mL<sup>-1</sup> and a Q-factor of 189 for this polarization multiplexing microfluidic platform. This work offers a unique avenue for enhanced accuracy and efficiency in terahertz biomedical trace analyzing via multidimensional sensing capabilities. Terahertz radiation ; Microfluidics ; Resonator ; Multiplexing ; Waveguide ; Figure of merit ; Lattice (music) ; Polarization (electrochemistry) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Jakub Bańczerowski
Source: Advances in Science and Technology – Research Journal (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Thermoplastic processing of grade 2 titanium: Finite element method simulations and experimental validation with the Anand model This work examined thermoplastic processing (TMP) of commercially pure Grade 2 titanium using finite element simulations with the Anand viscoplastic constitutive model and a broad experimental validation matrix (400-800 C; 0.01-10 s, including an intermediate 5 s case).The goal was to assess whether this model, originally developed for solder alloys, can capture the deformation behaviour of titanium at elevated temperatures, relevant to biomedical applications.Uniaxial compression tests were performed at 400-800 C and strain rates of 0.01-10 s, and the resulting stress-strain curves were compared with the FEM simulations in Ansys Workbench.Parameter calibration was based on experimental data, with focus on 500-600 C and 0.1-1 s -conditions previously shown to promote grain refinement and mechanical strengthening.The Anand model reproduced stress evolution well in the initial deformation phase, particularly at lower strain rates.Stress distributions in the FEM simulations captured the expected axial stress gradients (centre < side < edge), while stresses were typically 30-40 MPa higher than measured.The model also reproduced the elastic-plastic transition, with smoother behaviour observed at higher temperatures.However, the model does not account for dynamic recrystallisation (DRX) or related softening mechanisms, which leads to overestimation of stresses in the later stages of deformation.Experimental samples also displayed anisotropic deformation, likely due to initial material texture, which was not represented in the model.Despite these limitations, the Anand formulation demonstrates potential for predicting TMP of pure titanium, reducing reliance on trial-and-error testing and supporting process optimisation for biomedical applications. Finite element method ; Thermoplastic ; Work (physics) ; Deformation (meteorology) Microstructure and mechanical properties ; Advanced Welding Techniques Analysis ; Injection Molding Process and Properties
Authors: Qiang Zhang | Bijun Zhao | Linlin Liu | Mingwen Zhang | Yong Zhang | Yingke Ji | Puhui Zhang | Jianguo Wang | Xiaoqing Chen | Jianlin Zhao | Xuetao Gan
Source: Advanced Optical Materials (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Waveguide‐Integrated Multifunctional Photodetectors for In‐Sensor Computing ABSTRACT Emerging applications in the Internet of Things (IoT), autonomous vehicles, and robotics expose energy and latency bottlenecks in conventional photo‐sensing based on the von Neumann architecture. In‐sensor computing has emerged as a promising alternative by integrating multiple functions into a single device. 2D materials are widely used for prototypes owing to their excellent optoelectronic properties; however, free‐space illumination poses several challenges. First, the limited light–matter interaction length‐yields low absorption. Second, although logic and neuromorphic functions can combine electrical and optical signals, free‐space coupling hinders dense interconnection. Finally, these applications often require infrared operation, which remains challenging for commonly used transition metal disulfides (TMDs). Here, we report a waveguide‐integrated multifunctional photodetector for on‐chip in‐sensor computing that integrates light detection, nonvolatile memory, and reconfigurable logic/synaptic operations. Efficient infrared detection at 1552.5 nm is achieved, with a normalized photocurrent‐to‐dark‐current ratio (NPDR) up to 6.4 × 10 3 m W −1 . After negative control‐gate pulses programming, the device exhibits a projected retention of 21.1% after 10 years. We further realize an “AND” logic and synaptic short‐ and long‐term memory (STM/LTM). These results advance on‐chip in‐sensor computing compatible with high‐density photonic‐electronic interconnects and highlight a route toward systems that can sense, store, and compute at the point of capture. Neuromorphic engineering ; Photodetector ; Von Neumann architecture ; Latency (audio) ; Low latency (capital markets) ; Logic gate ; Infrared ; Energy (signal processing) ; Coupling (piping) 2D Materials and Applications ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: Jinyoung Park | Minseong Bae | Jeehye Na | Hyunwoo J. Kim
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Improving Large Molecular Language Model via Relation-aware Multimodal Collaboration Large language models (LLMs) have demonstrated their instruction-following capabilities and achieved powerful performance on various tasks. Inspired by their success, recent works in the molecular domain have led to the development of large molecular language models (LMLMs) that integrate 1D molecular strings or 2D molecular graphs into the language models. However, existing LMLMs often suffer from hallucination and limited robustness, largely due to inadequate integration of diverse molecular modalities such as 1D sequences, 2D molecular graphs, and 3D conformations. To address these limitations, we propose CoLLaMo, a large language model-based molecular assistant equipped with a multi-level molecular modality-collaborative projector. The relation-aware modality-collaborative attention mechanism in the projector facilitates fine-grained and relation-guided information exchange between atoms by incorporating 2D structural and 3D spatial relations. Furthermore, we present a molecule-centric new automatic measurement, including a hallucination assessment metric and GPT-based caption quality evaluation to address the limitations of token-based generic evaluation metrics (i.e., BLEU) widely used in assessing molecular comprehension of LMLMs. Our extensive experiments demonstrate that our CoLLaMo enhances the molecular modality generalization capabilities of LMLMs, achieving the best performance on multiple tasks, including molecule captioning, computed property QA, descriptive property QA, motif counting, and IUPAC name prediction. Property (philosophy) ; Language model ; Domain (mathematical analysis) ; Bridging (networking) ; Molecular graph ; Natural language ; Generalization ; Molecular imaging Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods