jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamad Baker Shoker | S. Ramakrishnan | Boris Croes | O. Crégut | Nicolas Beyer | Kokou D. Dorkenoo | P. Rodière | Björn Wehinger | Gastón Garbarino | Mohamed Mézouar | Marine Verseils | Pierre Fertey | S. Cherifi | Pierre Bouvier | Maël Guennou
Source: Physical Review Letters (preprint, 2026-01-02)
Field: Materials Science — Materials Chemistry

Reemergence of a Polar Instability at High Pressure in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>KNbO</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> Ferroelectric instabilities in perovskites are known to be suppressed by a moderate hydrostatic pressure. The prediction of their re-entrance in a much higher pressure regime is well accepted theoretically, but a conclusive experimental confirmation is still missing. Here, we show its occurrence in a classical but comparatively underlooked ferroelectric perovskite KNbO_{3}. We use single crystal x-ray diffraction, infrared and Raman spectroscopy, and second-harmonic generation to explore the phase transition sequence at high pressures up to 63 GPa. We show that polar cation displacements emerge locally but have to combine with tilts of the oxygen octahedra that are also developing under pressure. This results in a macroscopically centrosymmetric phase with an incommensurate modulation that reflects the tight competition between the polar and tilt instabilities. Soft modes associated with the tilts and the modulation, along with persistent order-disorder signatures, are clearly observed, suggesting that local disorder plays an important role in mediating this competition. Polar ; Instability ; Hydrostatic pressure ; Ferroelectricity ; Raman spectroscopy ; Perovskite (structure) ; Tilt (camera) ; Phase (matter) Ferroelectric and Piezoelectric Materials ; High-pressure geophysics and materials ; Crystal Structures and Properties
Authors: Nan Sheng
Source: arXiv (Cornell University) (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

A unified variational framework for the inverse Kohn-Sham problem The inverse Kohn-Sham (KS) problem seeks a local effective potential whose noninteracting ground state reproduces a prescribed electron density. Existing inversion formulations are often expressed in disparate languages, including reduced variational optimization, penalty regularization, response-based iteration, and PDE-constrained optimization. In this work, we develop a unified variational framework for inverse KS theory in two steps. First, we identify the fixed-density noninteracting constrained search embedded in exact density functional theory as the natural variational anchor of inverse KS inversion. In this setting, the KS potential appears as the variational dual object associated with density reproduction. Second, we show how the principal inversion formulations may be understood as realizations of the same inverse-KS structure and how they fit into a broader optimization-theoretic classification according to whether the KS state equations and density-reproduction condition are treated as objectives, constraints, penalties, or feasibility relations. Within this framework, Wu-Yang appears as a reduced exact-multiplier formulation, Zhao-Morrison-Parr as a quadratic-penalty relaxation, and PDE-constrained approaches as explicit state-constraint formulations. The same viewpoint also accommodates augmented-Lagrangian and all-at-once residual formulations, and clarifies the roles of additive-constant ambiguity, asymptotic normalization, nonsmooth variational structure, and weak-gap instability across inversion methods. Inverse problem ; Inverse ; Residual ; Inversion (geology) ; Dual (grammatical number) ; Variational method ; Calculus of variations Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Jinming Guo | Ce‐Wen Nan
Source: Microstructures (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

An effective index for high-performance piezoelectrics Piezoelectric materials have been attracting wide research interest for decades due to their ability to interconvert mechanical and electrical energy. Various mechanisms from different perspectives have been proposed to explain high piezoelectricity; however, a unified framework across diverse perovskite piezoelectric systems remains elusive. Yao et al. introduced a concept termed fluctuating local polarization (FLP) as an effective index for high piezoelectricity in perovskite ferroelectrics. FLP describes the composition-dependent piezoelectric performance in individual solid solutions and also accounts for high piezoelectricity across distinct systems. The FLP metric integrates the magnitude of local electric dipoles and the disorder of their orientations. This concept does not supersede traditional viewpoints but rather rationalizes them under a common microscopic index: local polarization flexibility. FLP is not merely an explanatory fingerprint of high piezoelectricity; it can also serve as a practical reference for engineering the next generation of ferroelectric materials. Piezoelectricity ; Polarization (electrochemistry) ; Dipole ; Ferroelectricity ; Metric (unit) ; Viewpoints Ferroelectric and Piezoelectric Materials ; Chemical and Physical Properties of Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Ziyi Huang | Wuliang Yin | Xiaochu Pang | Xinnan Zheng | Xiaohu Liu | Lisha Peng
Source: Sensors (article, 2026-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evaluation Method for Creep Damage of P92 Steel Based on Magnetic Barkhausen Noise and Magnetoacoustic Emission The application of ultra-supercritical power plant boilers is becoming increasingly widespread. P92 steel, as a typical material used for boiler main steam pipes, plays a critical role in unit safety, making the detection of its creep damage highly significant. However, existing conventional non-destructive testing methods are difficult to effectively detect creep damage. To address this issue, a magnetoacoustic emission (MAE)-magnetic Barkhausen noise (MBN) composite measurement system is developed, which is adapted to 20 Hz and 0.3 A sine wave excitation to trigger the synchronous pickup of MBN and MAE signals of P92 steel. After collecting signals with different creep life ratios (0%~100%) under working conditions of 650 °C and 100 MPa, time-domain (absolute mean, peak value, etc.) and frequency-domain (bandwidth) features are extracted. In response to the non-monotonicity between the magnetoacoustic features and the creep damage grade, principal component analysis (PCA) is introduced to reduce dimensionality. Different creep levels of samples in the two-dimensional principal component space are presented as clear gradient clustering, achieving the accurate differentiation of creep stages. Research has shown that the MAE-MBN composite system combined with PCA can effectively characterize the creep damage of P92 steel, providing a novel non-destructive detection path for the in-service life assessment of power plant components. Creep ; Barkhausen effect ; Noise (video) ; Acoustic emission ; Nondestructive testing ; SIGNAL (programming language) ; Barkhausen stability criterion ; Composite number Magnetic Properties and Applications ; Machine Fault Diagnosis Techniques ; Non-Destructive Testing Techniques
Authors: Kelsi Chesney | Jeffrey R. Whiteaker | Brian L. Hood | Ming Zhou | Huizen Zhang | Samuel Rivero-Hinojosa | A. Paulovich | Thomas Conrads | Brian Rood
Source: Cells (article, 2026-03-05)
Field: Chemistry — Spectroscopy

Longitudinal Detection of Tumor-Specific Peptides in Cerebrospinal Fluid for Pediatric Brain Tumor Surveillance Pediatric brain tumor survivors remain at high risk of recurrence, yet current surveillance strategies relying on neuroimaging and cerebrospinal fluid (CSF) cytology have limited sensitivity for early or minimal disease. Tumor-specific peptides (TSPs) derived from individual tumors represent a promising class of highly specific biomarkers for longitudinal disease monitoring through CSF-based proteomic analysis. In this study, tumor tissue and serial CSF samples from six pediatric brain tumor patients (five medulloblastomas and one atypical teratoid/rhabdoid tumor (ATRT)) were analyzed using an integrated proteogenomic workflow combining discovery and targeted mass spectrometry. TSPs were identified from resected tumor tissue and matched against shotgun CSF proteomic datasets to nominate candidate biomarkers. High-confidence peptides were synthesized as isotopically labeled standards and quantified longitudinally using targeted multiple reaction monitoring. Two TSP biomarkers derived from individualized pediatric brain tumors (one medulloblastoma and one ATRT) demonstrated robust detection in serial CSF samples and exhibited temporal concordance with radiographic disease course, declining with treatment response and increasing during disease progression. These findings establish the feasibility of detecting and longitudinally quantifying TSPs in CSF and support further investigation of individualized proteomic biomarkers for treatment response monitoring and disease surveillance in pediatric brain tumors. Cerebrospinal fluid ; Brain tumor ; Medulloblastoma ; Concordance ; Neuroimaging ; Proteomics ; Shotgun Advanced Proteomics Techniques and Applications ; Chromatin Remodeling and Cancer ; Glioma Diagnosis and Treatment
Authors: Jeong Sun Park | Jee-Young Pyo | Jina Kim | Seung Hyun Lee | Jong Woo Park | Seong-Ryul Kim | Seong-Wan Kim | Kee-Young Kim | Woori Kwak | Iksoo Kim
Source: Agriculture (article, 2026-02-08)
Field: Materials Science — Biomaterials

An Integrative Genome-Wide and Population-Based Analysis of Growth and Moltism Traits in a Core Strain Collection of the Silkworm (Bombyx mori) Understanding the genetic basis of growth and moltism in silkworm (Bombyx mori) is essential for improving silk production efficiency and elucidating the mechanisms underlying developmental plasticity. Thus, this study aimed to establish a collection of 20 representative B. mori core strains and perform integrative genomic analyses combining genome-wide association studies (GWASs) and population-specific variant detection. A total of 5,293,831 high-confidence single-nucleotide variants (SNVs) were identified across the population, and GWAS revealed significant associations between specific genetic loci and four growth-related traits: larval weight at day 7 of the fifth instar, pupal weight, cocoon weight, and cocoon layer weight. Among these, two missense variants within the Cycb gene were significantly correlated with increased body weight at the late fifth instar stage, suggesting a potential role for this isoform in regulating cell-cycle-driven tissue expansion during rapid larval growth. Moreover, a population-based comparison identified 2803 trimolter-specific missense SNVs in 1440 genes, of which 109 were functionally annotated. Notably, homozygous variants were detected in key developmental regulators, such as MET1 and TOR1, implying potential alterations in juvenile hormone signaling and nutrient-dependent growth pathways that may contribute to the dominant trimolter phenotype. Although experimental validation remains necessary, these findings provide a genomic framework for understanding the molecular mechanisms underlying moltism variation and offer valuable resources for future silkworm genetic improvement. Missense mutation ; Genome-wide association study ; Larva ; Gene ; Genetic variation ; Genetic association ; Phenotype Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Neurobiology and Insect Physiology Research
Authors: K S Varun | Rakesh Vunnam
Source: ArXiv.org (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Can Large Language Models Solve Engineering Equations? A Systematic Comparison of Direct Prediction and Solver-Assisted Approaches Transcendental equations requiring iterative numerical solution pervade engineering practice, from fluid mechanics friction factor calculations to orbital position determination. We systematically evaluate whether Large Language Models can solve these equations through direct numerical prediction or whether a hybrid architecture combining LLM symbolic manipulation with classical iterative solvers proves more effective. Testing six state-of-the-art models (GPT-5.1, GPT-5.2, Gemini-3-Flash, Gemini-2.5-Lite, Claude-Sonnet-4.5, Claude-Opus-4.5) on 100 problems spanning seven engineering domains, we compare direct prediction against solver-assisted computation where LLMs formulate governing equations and provide initial conditions while Newton-Raphson iteration performs numerical solution. Direct prediction yields mean relative errors of 0.765 to 1.262 across models, while solver-assisted computation achieves 0.225 to 0.301, representing error reductions of 67.9% to 81.8%. Domain-specific analysis reveals dramatic improvements in Electronics (93.1%) due to exponential equation sensitivity, contrasted with modest gains in Fluid Mechanics (7.2%) where LLMs exhibit effective pattern recognition. These findings establish that contemporary LLMs excel at symbolic manipulation and domain knowledge retrieval but struggle with precision-critical iterative arithmetic, suggesting their optimal deployment as intelligent interfaces to classical numerical solvers rather than standalone computational engines. Computation ; Iterative method ; Exponential function ; Position (finance) ; Numerical analysis ; Transcendental equation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Leandro Coloma | Rodrigo Sucasaca | Jorge Luis Apaza | Yuri Silva Vidal Lester | Angel Joel Mendoza Quispe | Christofer Alex Diaz Arapa
Source: Advances in Science and Technology – Research Journal (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Investigation effect of copper particle size and jaw geometric configurations on jaw crusher wear The growing demand for efficiency in industrial crushing processes has highlighted the need to understand the particle behaviour in jaw crushers in order to minimise wear and maximise productivity.In response, analysing the influence of particle size and jaw geometry can play a crucial role in reducing operating costs and increasing equipment life.This study explored the relationship between different material sizes as well as the geometric configurations of movable and fixed jaws in the wear of jaw crushers.A comparative analysis was performed using simulation to evaluate wear through a discrete element method-multibody dynamics-particle replacement method (DEM-MBD-PRM) coupling with the Tavares fracture model and the Archard wear model.Prior to simulation, the material and contact parameters were calibrated in EDEM software to ensure reliable representation of copper-steel interactions.This analysis process led to a better understanding of tribological behaviour.The configurations with smaller particle sizes, in the range of 80 to 100 mm, reduced wear by up to 50% compared with larger particles, in the range of 100 to 120 mm, with distinct wear patterns observed between sharp and standard profiles.Additionally, simulation showed that jaws with sharp profiles typically had longer service lives, suggesting that the configurations with standard profiles and large particle sizes are the most susceptible to wear.The findings of this study demonstrate that optimising feed particle size and jaw geometry can reduce wear on crusher components by up to 50%, directly contributing to more efficient crushing operations, extended equipment lifetime, and lower maintenance costs.Although the study was uniquely based on computational modelling without an experimental verification, the results provide a solid framework for the guidance of future physical testing and design improvements. Crusher ; Particle size ; Copper ; Particle (ecology) ; Geometric shape ; Particle-size distribution Metal Alloys Wear and Properties ; Mineral Processing and Grinding ; Powder Metallurgy Techniques and Materials
Authors: Woo-Hyeong Yu | Min-Jee Kim | Ahyeong Lee | Hong-Gu Lee | Byoung-Kwan Cho | Hoyoung Lee | Changyeun Mo
Source: Applied Sciences (article, 2026-03-20)
Field: Chemistry — Analytical Chemistry

Development of a Nondestructive Classification Model for Citrus Fruit External Defects Using Hyperspectral Imaging and Wavelength Selection Algorithm External defects considerably reduce the quality, consumer acceptance, and market value of citrus fruits. Therefore, a rapid and reliable, non-destructive inspection method is necessary for postharvest processing. In this study, a non-destructive approach for external defect classification of citrus fruits is developed by combining visible–near infrared hyperspectral imaging (HSI) with effective wavelength selection (EWS) algorithms. First, 1702 spectral samples of normal and defective regions on citrus fruit surfaces were collected. A partial least squares discriminant analysis (PLS-DA) model was developed using the full wavelength range (400–1000 nm), which achieved 99.02% prediction accuracy. Four EWS algorithms—weighted regression coefficients, variable importance in projection, sequential forward selection (SFS(5, 10, 15)), and random frog—were evaluated for optimal spectral dimensionality and computational efficiency. The SFS15-PLS-DA model, which selected 15 optimal variables out of the initial 300 and used maximum normalization preprocessing, achieved the highest prediction accuracy of 99.80%. This model demonstrated near-perfect classification while reducing the total number of wavelengths by 95.0% (from 300 to 15 wavelengths). Further, a pixel-wise image classification algorithm was implemented using the optimal model, which effectively detected physical damage, pest infestation, and fungal decay. These results demonstrate that combining HSI with EWS enables compact, interpretable, and high-performance models suitable for real-time postharvest sorting. This approach has strong potential to enhance automation, speed, and reliability in commercial citrus quality assessment. Hyperspectral imaging ; Postharvest ; Normalization (sociology) ; Partial least squares regression ; Pattern recognition (psychology) ; Variable elimination ; Dimensionality reduction ; Feature selection ; Linear discriminant analysis Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Padma P. Tadi Uppala | Elmer C. Rivera | Hyun Kwon | Sharon S. Lum
Source: Sensors (article, 2026-01-08)
Field: Chemistry — Spectroscopy

Serum Protein Signatures for Breast Cancer Detection in Treatment-Naïve African American Women Using Integrated Proteomics and Pattern Analysis Breast cancer is the leading cause of cancer-related mortality in African American (AA) women. In this study we evaluated the serum proteomic profile of AA women with breast cancer using an integrated proteomic framework with multivariate pattern analysis. Using 2D-DIGE, thousands of serum protein spots were detected across 33 gels; 46 spots met criteria for presence, statistical significance, and differential expression. Proteins from the spots were identified by MALDI-TOF/TOF and matched in curated databases, highlighting serum biomarkers including ceruloplasmin, alpha-2-macroglobulin, complement component C3 and C6, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein and haptoglobin-related protein. LC-MS/MS analysis revealed 163 differentiating peptides after imputing and filtering 286 peptides. These were evaluated using cumulative distribution function (CDF) analysis, a nonparametric method suited for limited sample sizes. Peptide patterns were explored with Random Forest, showing concordance with CDF. The model achieved an AUC of 0.85 at the peptide level. This workflow identified differentiating proteins (CERU, A2MG, CO3, VTDB, HEMO, APOB, APOA4, CFAH, CO4A, AACT, K1C10, ITIH2, ITIH4), highlighting CERU, A2MG, and CO3 with overexpression and reproducible identification across platforms. We present an integrated, non-invasive serum protein biomarker signature panel specific to AA women, through reproducible proteomic sensor framework to support early detection and breast cancer prevention. Proteomics ; Breast cancer ; Biomarker ; Cancer ; Proteome ; Biomarker discovery ; Concordance Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Luciano Feo | Giuseppe Lovisi | Rosa Penna
Source: Mathematics (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

A Comprehensive Integral-Form Framework for the Stress-Driven Non-Local Model: The Role of Convolution Kernel, Regularization and Boundary Effects This manuscript presents a study of the Stress-Driven integral Model (SDM) for the bending response of Bernoulli–Euler nanobeams. Unlike conventional approaches that reformulate the nonlocal integral problem into an equivalent differential form, a direct numerical strategy is developed to solve the integral equation. The proposed framework enables a systematic comparison of six different convolution kernels (Helmholtz, Gaussian, Lorentzian, triangular, Bessel and hyperbolic cosine), highlighting how their mathematical properties influence the structural response. To address issues related to long-range interactions and the potential ill-posedness of the integral operator, an adaptive regularization procedure based on the Morozov discrepancy principle is introduced. Furthermore, a local clipping and renormalization technique is proposed to properly account for boundary effects while preserving the weighted averaging property of the kernels. Validation against available analytical solutions for the Helmholtz kernel demonstrates high accuracy, with errors below 1%. The results show that the nonlocal parameter significantly affects structural rigidity depending on the kernel shape and that the proposed approach ensures consistent convergence to the local solution as the nonlocal parameter tends to zero. Regularization (linguistics) ; Kernel (algebra) ; Bessel function ; Boundary (topology) ; Convolution (computer science) ; Integral equation ; Trigonometric functions Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Material Mechanics
Authors: Haowei Ti | Yu Ti | Bingchen Li | Xinyu Zhao
Source: Health Medicine and Therapeutics (article, 2026-03-02)
Field: Materials Science — Biomaterials

Artificial Bionic Blood Vessel and Manufacturing Method Objective: This study provides an artificial bionic blood vessel and a manufacturing method thereof, which includes a three-layer artificial bionic blood vessel body, wherein the three-layer structure of the artificial bionic blood vessel is a natural silk layer, a diluted liquid silicone layer and a braided tube layer, wherein the diluted liquid silicone layer is located on the inner side of the natural silk layer, and the braided tube layer is located on the outer side of the natural silk layer. Materials and Methods: The braided tube layer is made of catgut braiding. Artificial bionic blood vessels are mainly composed of animal tissues and natural organisms. They will not have side effects on body functions and are easily absorbed by the body. Fish skin glue oligopeptide coatings are distributed at the joints of bionic blood vessels to promote blood vessel regeneration. Discussion: The outermost layer is a superfine catgut braided tube. After a period of artificial blood vessel replacement, the catgut braided tube will be absorbed and covered by the regenerated blood vessels. Conclusion: The material design system that introduces artificial intelligence is different from the traditional, rigid design thinking. Blood vessel ; Tube (container) ; Silicone ; Layer (electronics) ; Artificial skin ; Blood circulation Silk-based biomaterials and applications ; 3D Printing in Biomedical Research ; Cephalopods and Marine Biology