jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arpa Ebrahimi | Shuxin Chi | Jun Yang | Phoebe Y. Lee | Prongbaramee K. Colling | Hailey Mendenhall | Zejing Wang | Chad Giusti | Leonard J. Foster | David A. Hendrix | Claudia S. Maier
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-26)
Field: Chemistry — Spectroscopy

Single-cell proteomics reveals proteome remodeling and cellular heterogeneity during NGF-induced PC12 neuronal differentiation 1 Abstract Single-cell proteomics (SCP) enables direct measurement of cellular heterogeneity during dynamic biological processes. Here, we applied an SCP workflow to investigate proteome diversity during nerve growth factor (NGF)–induced differentiation of PC12 cells. Differentiated PC12 cells are highly adherent and prone to aggregation, complicating single-cell sample preparation. To address this challenge, sample handling was optimized using gentle dissociation, anti-adhesive conditions, and rapid processing immediately prior to cell isolation. Individual cells were deposited using a refined thermal inkjet (TIJ) dispensing system, enabling accurate single-cell placement with minimal sample loss. Inclusion of the mild nonionic surfactant n-dodecyl-β-D-maltoside (DDM) improved recovery of membrane-associated and other low-solubility proteins. Coupled with high-sensitivity liquid chromatography–ion mobility–mass spectrometry, this workflow consistently quantified approximately 2,000–3,000 proteins per cell across differentiation stages. Single-cell proteomic profiles acquired over the differentiation time course revealed clear separation between undifferentiated and NGF-treated cells by Day 6. At later stages (Days 4–6), cells further partitioned into two distinct subpopulations with protein expression patterns not evident in bulk measurements. Dimensionality reduction and non-negative matrix factorization identified multiple proteomic states coexisting within the same differentiation stages, characterized by coordinated differences in pathways related to intracellular trafficking, protein translation, and neuronal structural organization. Together, these results show that while global proteome remodeling during PC12 differentiation is captured in both bulk and single-cell data, single-cell proteomics uniquely resolves functionally distinct cellular subpopulations that are masked in population-averaged analyses. Proteome ; Proteomics ; Quantitative proteomics ; Cellular differentiation ; Cell ; Intracellular ; Cell type Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Biotin and Related Studies
Authors: Zlatko Pangarić
Source: Preprints.org (preprint, 2026-03-09)
Field: Chemistry — Physical and Theoretical Chemistry

Multidimensional Analysis of the Periodic System of Elements Using the Symbolic Difference Structure (SSD) Method This paper investigates the application of the Symbolic Difference Structure (SSD) method as an innovative tool for multidimensional analysis and classification of elements in the periodic system. By applying the SSD algorithm ($k=3$) to five key physicochemical properties—atomic mass, electronegativity, first ionization energy, number of unpaired electrons, and melting point—the study demonstrates that the periodic system exhibits a recognizable numerical geometry that can be systematically quantified. The results show that SSD classification does not alter established chemical periodicity but enables: (1) quantitative discrimination between genuine nuclear anomalies and metrological artifacts, (2) identification of characteristic “hotspots’’ where multiple properties change simultaneously, and (3) a heuristic framework for predicting properties of heavy and superheavy elements. The analysis is extended using monoisotopic masses and correlation studies, confirming the diagnostic value of the method and opening new pathways for comparative analysis in materials science and chemical education. Multidimensional analysis ; Monoisotopic mass ; Identification (biology) ; Heuristic ; Key (lock) ; Basis (linear algebra) ; Symbolic data analysis ; Similarity (geometry) History and advancements in chemistry ; Chemical Thermodynamics and Molecular Structure ; Machine Learning in Materials Science
Authors: Yuting He | Jinbao Jiang | Feng Xiong | Z. Q. Zhu
Source: Nanomaterials (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Dynamically Reconfigurable XNOR/IMP Logic Based on Dual-Mechanism Operation in an Electrically Tunable Two-Dimensional Heterojunction Reconfigurable logic is crucial for future adaptive computing, but is challenging to realize with conventional complementary metal-oxide-semiconductor technology due to the limited field-effect characteristics of the fundamental silicon devices. Two-dimensional materials offer a promising platform, yet enhancing their functional versatility requires novel operational mechanisms. Here, we demonstrate a single WSe<sub>2</sub>/h-BN/graphene heterojunction capable of dynamically switching between distinct logic functions-XNOR and IMP (implication gate or "IF-THEN" gate)-simply by modulating the drain-source voltage. At a low bias of 0.3 V, the carrier distribution is governed by capacitive coupling, realizing an XNOR gate. Increasing the bias to 3 V activates Fowler-Nordheim tunneling between the graphene floating gate and the drain, enabling IMP logic operation. The interplay and voltage-induced transition between these two physical mechanisms underpin the device's multifunctional capability. This work introduces a novel operational strategy for two-dimensional material-based reconfigurable logic, providing a pathway toward compact, adaptive hardware for post-CMOS computing. XNOR gate ; Logic gate ; Heterojunction ; Capacitive sensing ; NOR gate ; AND gate ; Pass transistor logic ; Graphene 2D Materials and Applications ; Graphene research and applications ; Topological Materials and Phenomena
Authors: Zhe Chen | Peilin Zheng | Wenshuo Chen | Xiucheng Wang | Yutao Yue | Nan Cheng
Source: arXiv (Cornell University) (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Electromagnetic Fields for High-Resolution Material Parameter Reconstruction Creating functional Digital Twins, simulatable 3D replicas of the real world, is a central challenge in computer vision. Current methods like NeRF produce visually rich but functionally incomplete twins. The key barrier is the lack of underlying material properties (e.g., permittivity, conductivity). Acquiring this information for every point in a scene via non-contact, non-invasive sensing is a primary goal, but it demands solving a notoriously ill-posed physical inversion problem. Standard remote signals, like images and radio frequencies (RF), deeply entangle the unknown geometry, ambient field, and target materials. We introduce NEMF, a novel framework for dense, non-invasive physical inversion designed to build functional digital twins. Our key insight is a systematic disentanglement strategy. NEMF leverages high-fidelity geometry from images as a powerful anchor, which first enables the resolution of the ambient field. By constraining both geometry and field using only non-invasive data, the original ill-posed problem transforms into a well-posed, physics-supervised learning task. This transformation unlocks our core inversion module: a decoder. Guided by ambient RF signals and a differentiable layer incorporating physical reflection models, it learns to explicitly output a continuous, spatially-varying field of the scene's underlying material parameters. We validate our framework on high-fidelity synthetic datasets. Experiments show our non-invasive inversion reconstructs these material maps with high accuracy, and the resulting functional twin enables high-fidelity physical simulation. This advance moves beyond passive visual replicas, enabling the creation of truly functional and simulatable models of the physical world. Inversion (geology) ; Differentiable function ; Point (geometry) ; Reflection (computer programming) ; Key (lock) ; Physical system ; Field (mathematics) ; Inverse problem Metamaterials and Metasurfaces Applications ; Microwave Imaging and Scattering Analysis ; Augmented Reality Applications
Authors: Aya MIDAOUI | Cyrille Baudouin | Ludivine Maccioni | Florence Danglade | Régis Bigot
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of a digital twin for real-time 3D temperature field monitoring: Application to the Jominy end-quench test This paper presents the development of a digital twin designed for “real-time” monitoring of the three-dimensional temperature field during the Jominy end-quench test. This test, commonly used to assess the hardenability of steels, generates a pronounced axial thermal gradient, making it an ideal case study for validating a digital twin approach. The core of the methodology relies on the construction of a surrogate model based on Proper Orthogonal Decomposition (POD), derived from Finite Element simulations (FEM) performed with Forge NxT®. This reduced-order model enables the reconstruction of the full temperature field from a limited number of surface measurements acquired via an infrared camera. An inverse formulation, based on least-squares optimization, is used to estimate the modal coefficients required for reconstructing the thermal field. The approach is validated both numerically and experimentally. Temperature deviations between simulated and reconstructed fields generally remain below $$\:\pm\:20\:^\circ\:C$$ , with larger localized errors near the air/water transition zone. A real-time visualization interface, developed in UNITY 3D®, allows the operator to monitor thermal evolution, including within the core of the specimen. This work demonstrates the feasibility of “real-time” thermal monitoring paving the way for better monitoring of thermomechanical paths during hot forging, particularly for superalloys. Field (mathematics) ; Development (topology) ; Hardenability ; Test (biology) ; Temperature measurement Magnetic Properties and Applications ; Thermography and Photoacoustic Techniques ; Geothermal Energy Systems and Applications
Authors: Tianhao Wang | Chengcong Ma | Xiangjun Xu | Xuanbing Qiu | Ye Teng
Source: Preprints.org (preprint, 2026-02-28)
Field: Chemistry — Analytical Chemistry

Nondestructive Quantification of Soluble Solid Content in ‘Red Fuji’ Apples Using Near-Infrared Diffuse Reflectance Spectroscopy with a Low-Cost Embedded Spectrometer Soluble solid content (SSC) is a critical indicator of ‘Red Fuji’ apple quality, directly governing fruit grading and maturity assessment processes. Conventional SSC measurement by refractometry is destructive and time-consuming, rendering near-infrared diffuse reflectance spectroscopy (NIR-DRS) a promising nondestructive alternative. In this study, a low-cost and compact embedded spectrometer named as DLP NIR-scan Nano EVM was used to acquire NIR-DRS spectra of ‘Red Fuji’ apples for SSC prediction. To improve prediction accuracy, we combined spectral preprocessing with machine learning methods. The dataset was cleaned using Monte Carlo outlier detection, and samples were divided into calibration and validation sets via Kennard–Stone (KS) and joint X-Y distance (SPXY) algorithms. Among preprocessing methods tested, a 12-point second derivative performed best when paired with KS partitioning. For feature-wavelength selection on the preprocessed KS data, competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, and Random Frog were applied to the second-derivative spectra. Partial least squares regression (PLSR) models were then built using both full-spectrum data and four sets of selected wavelengths. The best preprocessed PLSR model achieved R2c = 0.916, RMSEC = 0.4093%, R2p = 0.8632, and RMSEP = 0.537%. These results demonstrate that NIR-DRS, combined with appropriate preprocessing and modeling strategies, offers a reliable, rapid, and nondestructive method for apple SSC quantification, paving the way for portable, cost-effective instruments for commercial fruit quality monitoring. Partial least squares regression ; Monte Carlo method ; Calibration ; Nondestructive testing ; Spectrometer ; Diffuse reflectance infrared fourier transform ; Outlier ; Preprocessor ; Spectroscopy ; Hyperspectral imaging Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Yan Wang | Yifan Du | Xingwei Liu | Jianxing Liu
Source: Technologies (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Numerical Simulation Study on Region Tracking of Jet Formation and Armor-Piercing Process of Zirconium Alloy Shaped Charge Liner Zr alloy-shaped charge liners (SCLs) offer broad application prospects due to their multiple post-penetration damage effects. However, research on these liners is still in its early stages. The mechanisms of jet formation and penetration for Zr alloys SCL remain unclear, and the specific contribution of different liner regions to the penetration process is not yet understood. This gap in knowledge has limited their structural design to a black-box correlation between global structural parameters and macroscopic penetration efficiency. To address this gap, a region-tracing Smoothed Particle Hydrodynamics (SPH) simulation was employed. Following a strategy of “wall thickness layering + axial segmentation,” the Zr alloy liner was partitioned into ten characteristic regions. This methodology facilitated the tracking of material transport from each region during jet formation and penetration into an AISI 1045 steel target. The contribution of each region to the penetration depth was then quantitatively assessed via post-processing. For the first time, the “critical region” contributing most to penetration depth was identified, and the influence of the liner’s cone angle and wall thickness on the contribution of each region was revealed. This study enhances the theoretical framework for understanding the damage effects of Zr alloy shaped charge liners. It not only advances the fundamental understanding of jet penetration mechanisms but also provides a theoretical basis for the refined design and performance optimization of these liners. Shaped charge ; Penetration (warfare) ; Penetration depth ; Alloy ; Computer simulation ; Zirconium High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; Nuclear Materials and Properties
Authors: Jinlin Chen | Yiquan Wu | Xinmei Xu
Source: Separations (article, 2026-01-16)
Field: Chemistry — Spectroscopy

Real-Time Correction Algorithm for a Chromatographic Background Based on Numerical Algorithm Although numerous baseline correction methods exist, most are confined to static post-elution processing and fail to meet real-time analysis requirements. To address this, we propose a real-time baseline estimation method based on the Informer time-series prediction model that performs correction during data acquisition without waiting for complete elution. Our work focuses on three key aspects: chromatographic dataset construction, model training, and baseline prediction. Simulation experiments demonstrate that the proposed method achieves comparable accuracy to conventional static processing approaches while exhibiting significant real-time advantages. In processing real chromatographic data, the model achieves a 98.3% chromatographic peak retention rate, with a single computation time of approximately 35 ms—substantially shorter than typical chromatographic sampling cycles (600–900 ms), thus fully satisfying the quantitative analysis requirements for real-time background subtraction. Computation ; Sampling (signal processing) ; Key (lock) ; Baseline (sea) ; Data processing ; Retention time Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Hyung-Wook Choi | Byeongchan Lee | Un-Beom Kang | Sunghyun Huh
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-08)
Field: Chemistry — Spectroscopy

msmu: a Python toolkit for modular and traceable LC-MS proteomics data analysis based on MuData Abstract Computational workflows for MS-based proteomics remain comparatively fragmented, with heterogeneous data formats and analysis pipelines that hinder their reproducibility, interoperability, and reuse of processed data. We present msmu, an open-source Python package that implements a flexible and reproducible end-to-end pipeline for post-search data preprocessing and statistical analysis. At its core, msmu leverages the highly structured MuData format, empowering comprehensive data provenance, transparency in data sharing and reuse, and interoperability with broader Python ecosystem. Together, msmu represents a unique and significant step toward realizing the FAIR (Findable, Accessible, Interoperable, and Reusable) principles in computational proteomics. Python (programming language) ; Interoperability ; Workflow ; Modular design ; Reuse ; Preprocessor ; Standardization ; Pipeline (software) Advanced Proteomics Techniques and Applications ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Shufan Chen | Yubin Fan | Hao Li | Xiaodong Qiu | Ben Wang | Lijian Zhang | Shumin Xiao | Din Ping Tsai
Source: Light Science & Applications (article, 2026-01-12)
Field: Materials Science — Surfaces, Coatings and Films

Meta-device for sensing subwavelength lateral displacement Accurate transverse displacement measurement is essential for precise mask-to-wafer positioning in lithography. While lateral displacement metrology has achieved nanometer-level precision, the limitations imposed by coherent state and grating challenge in-situ measurement speed and precision. Here, we introduce a two-photon state transverse displacement measurement method utilizing a polarization gradient metasurface by employing two-photon state interference. Compared with the classical method, our new method can experimentally reduce the number of detected photons to around 3% with equivalent precision. These attributes make the two-photon state polarization gradient metasurface approach highly suitable for integration with semiconductor lithography processes and show its promise in realizing equivalent measurement precision within notably shorter acquisition durations, providing a robust solution for next-generation transverse displacement measurement requirements. Metrology ; Transverse plane ; Displacement (psychology) ; Polarization (electrochemistry) ; Grating ; Lithography ; Nanometrology Optical Coatings and Gratings ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Wei Jiang | Yao Liu | Shaogeng Zeng | Zhongyan Liu | Jinfang Liu | Shuwen Wang
Source: Acta Scientiarum Polonorum Technologia Alimentaria (article, 2026-03-04)
Field: Chemistry — Analytical Chemistry

Identification of Thawed Fish Meat Based on Near-Infrared Spectroscopy Combined with DAG-LSTMSVM Model The market price of aquatic products is closely linked to their sensory freshness.This relationship is especially pronounced in fish: without timely post-harvest processing, microorganisms can rapidly proliferate, leading to spoilage, quality deterioration, and a sharp decline in freshness and marketability (Zhang and Jiao, 2021).Freezing is widely used to preserve the fish quality; however, repeated freeze-thaw cycles cause substantial degradation and significantly reduce product value (Cai et al., 2019;Hafezparast-Moadab et al., 2018).Because chilled fresh fish and frozenthawed fish often appear similar, some merchants sell thawed fish as fresh fish, violating consumer rights, disrupting market order, and potentially creating foodsafety risks (Lu, 2022;Sales et al., 2020).Studies have shown that freeze-thaw processes lead to juice Fish <Actinopterygii> ; Identification (biology) ; Spectroscopy ; Fish fillet Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Water Quality Monitoring and Analysis
Authors: Pawel Wieczorkiewicz | Tadeusz M. Krygowski | Halina Szatylowicz
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Anatomy of the substituent effect in complex N-heterocycles The substituent effect in heterocyclic compounds is a critical concept in chemistry, influencing the properties and reactivity of these molecules. In simple systems, like benzene derivatives, it is well-understood. However, in heterocycles with multiple substituents and fused rings-important nature's building blocks-the situation becomes complex and hard to comprehend. In this work, quantum-chemical calculations (at the DSD-PBEP86-D3BJ/def2-TZVPP level of theory), including substituent effect descriptors and the electron density of delocalized bonds decomposition scheme, were performed on derivatives of cytosine, isocytosine, guanine, isoguanine, thioguanine, hypoxanthine and 5-aza-7-deazaguanine in different tautomeric forms, allowing the determination and quantitative comparison of the strength of different interactions between substituents and heteroatomic fragments in each studied molecule. The comparisons allowed formulating several rules that help understand and predict the strength, and the resonance/inductive nature of the substituent effect in complex N-heterocyclic systems. Substituent ; Delocalized electron ; Tautomer ; Benzene ; Reactivity (psychology) ; Density functional theory ; Electron delocalization Chemical Reaction Mechanisms ; Synthesis and Properties of Aromatic Compounds ; Cyclization and Aryne Chemistry
Authors: Jia Hang Wu | Tze Ching Ong | Pauline Ong | Kee Huong Lai | Nur Fathieyah Sulaiman
Source: Journal of Engineering Technology and Applied Physics (article, 2026-03-15)
Field: Chemistry — Analytical Chemistry

A Rule-Based Approach for Oil Palm Fruit Ripeness Detection using Machine Vision In the oil palm industry, the grading process of oil palm fruits is conducted manually by trained inspectors via visual examination. Laborious and prone to human error, this process results in fruit ripeness being determined subjectively based on some developed standards. This study presents the development of an automatic grading system for oil palm fruit ripeness using red-green-blue (RGB) channel and rule-based classification. The grading system consists of four main stages: (i) image acquisition using camera and computer; (ii) image processing involving the segmentation of oil palm fruit; (iii) calculation of mean colour intensity based on the RGB colour model; and (iv) determination of oil palm fruit ripeness using the rule-based classification. Several features are extracted from the RGB channel and based on these extracted features; simple classification rules are formed to identify fruit ripeness. The maturity of oil palm fruits is then classified into two categories, ripe and unripe. Thirty-nine samples of oil palm fruits, comprising 21 ripe fruits and 18 unripe ones, are used to develop the classification rule. A graphical user interface (GUI) is developed using Matlab software to assist with the grading system whereby all the relevant image processing steps are coded into the GUI. The validity of the grading system is tested using nine samples of oil palm fruits (four ripe and five unripe) and the classification accuracy, sensitivity and specificity of 77.78 – 88.89%, 75%, and 80 – 100% respectively are achieved based on the established classification rule. Performance comparison with fuzzy logic indicates the promising potential of these simple classification rules as well. Ripeness ; Palm oil ; RGB color model ; Machine vision ; Grading (engineering) ; Image processing ; Palm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Oil Palm Production and Sustainability