jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mostafa Kalhor | Cemil Can Saylan | Mario Picciani | Lutz Fischer | Falk Boudewijn Schimweg | Joel Lapin | Juri Rappsilber | Mathias Wilhelm
Source: Nature Communications (article, 2025-07-01)
Field: Chemistry — Spectroscopy

Prosit-XL: enhanced cross-linked peptide identification by fragment intensity prediction to study protein interactions and structures It has been shown that integrating peptide property predictions such as fragment intensity into the scoring process of peptide spectrum match can greatly increase the number of confidently identified peptides compared to using traditional scoring methods. Here, we introduce Prosit-XL, a robust and accurate fragment intensity predictor covering the cleavable (DSSO/DSBU) and non-cleavable cross-linkers (DSS/BS3), achieving high accuracy on various holdout sets with consistent performance on external datasets without fine-tuning. Due to the complex nature of false positives in XL-MS, an approach to data-driven rescoring was developed that benefits from Prosit-XL's predictions while limiting the overestimation of the false discovery rate (FDR). After validating this approach using two ground truth datasets consisting of synthetic peptides and proteins, we applied Prosit-XL on a proteome-scale dataset, demonstrating an up to ~3.4-fold improvement in PPI discovery compared to classic approaches. Finally, Prosit-XL was used to increase the coverage and depth of a spatially resolved interactome map of intact human cytomegalovirus virions, leading to the discovery of previously unobserved interactions between human and cytomegalovirus proteins. Interactome ; False positive paradox ; Proteome ; Fragment (logic) ; Computational biology ; Peptide ; Computer science ; False discovery rate ; Limiting ; Identification (biology) ; Data mining ; Chemistry ; Bioinformatics ; Artificial intelligence ; Biology ; Algorithm ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics
Authors: Klaus Welters | Christian Thoben | Christian‐Robert Raddatz | Florian Schlottmann | Stefan Zimmermann | Detlev Belder
Source: Electrophoresis (article, 2025-04-28)
Field: Chemistry — Spectroscopy

Coupling Capillary Electrophoresis With a Shifted Inlet Potential High‐Resolution Ion Mobility Spectrometer We present the coupling of capillary electrophoresis to a custom-built high-resolution ion mobility spectrometer (IMS). This system integrates a shifted inlet potential IMS configuration with a customised nanoflow ESI sheath interface. It enables the rapid analysis of quaternary ammonium compounds (QACs) and their impurities in real-world samples. It allowed the detection of six non-chromophoric compounds in about 3 min. The assignment of the IMS signals to compounds was supported by matching experimentally determined collision cross-section (CCS) values with predicted values. The system achieved a detection limit in the single-digit picogram range with IMS resolutions of over 80. Capillary electrophoresis ; Detection limit ; Ion-mobility spectrometry ; Spectrometer ; Chemistry ; Analytical Chemistry (journal) ; Electrophoresis ; Capillary action ; Resolution (logic) ; Coupling (piping) ; Mass spectrometry ; Impurity ; Inlet ; High resolution ; Chromatography ; Materials science ; Optics ; Physics ; Computer science ; Remote sensing Mass Spectrometry Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications ; Analytical Chemistry and Chromatography
Authors: 许舰 Xu Jian | 郭泽林 Guo Zelin | 江敏 Jiang Min
Source: Laser & Optoelectronics Progress (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

基于一种残差网络模型的超表面透射光谱预测方法 超表面是一种人工设计的二维平面结构。设计超表面结构需要在软件中进行仿真，耗费较长的时间。为了解决耗时长的问题，提出一种结合混合域注意力机制的残差网络模型以预测超表面的透射光谱。通过构建多种金超表面结构数据集并进行预处理，来搭建这一残差网络模型。实验结果显示：模型预测的透射光谱与仿真结果高度一致，测试集平均绝对误差为0.0069，预测准确率超过98.7%。相比于其他深度学习模型，该模型在预测精度和效率上均有显著提升，证明其具有高效性和准确性，为超表面快速设计优化提供新方法。 Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Riski Afandi
Source: Journal of Chemical Learning Innovation. (article, 2025-06-24)
Field: Chemistry — Electrochemistry

Practical Spectrophotometry: Exploring Maximum Absorption Peaks of Fe(SCN)₃ and CuSO₄ Solutions Using Visible Light Purpose of the study: This study explores simple visible light spectroscopy to measure maximum absorption wavelengths in Fe(SCN)₃ and CuSO₄ solutions, analyzing the impact of light filters on absorption spectra. Methodology: This study exposed Fe(SCN)₃ and CuSO₄ solutions to tungsten lamp light, passing through a collimator and diffraction grating. Spectral data, recorded by a camera, compared filtered and unfiltered conditions. Absorbance, determined from light intensity differences, revealed characteristic wavelengths with maximum absorbance for each solution, visualized through wavelength-absorbance graphs. Main Findings: The maximum absorption wavelengths for Fe(SCN)₃ were 481 nm (with filter) and 472 nm (without filter), while for CuSO₄, they were 570 nm (with filter) and 553 nm (without filter). Monochromatic filters enhanced accuracy, aligning Fe(SCN)₃ closer to theoretical values and reducing CuSO₄ deviations to 20 nm (filtered) compared to 37 nm (unfiltered). Novelty/Originality of this study: The novelty of this study lies in the development of a simple visible light-based spectrophotometer by utilizing easily accessible tools and materials, such as tungsten lamps and CMOS cameras, to understand the maximum absorption peaks of Fe(SCN)₃ and CuSO₄ solutions. In addition, this study explores the effect of using light filters on the accuracy of spectrum data, providing a practical alternative for spectroscopy experiments in resource-limited learning environments. Visible spectrum ; Absorbance ; Wavelength ; Monochromatic color ; Absorption (acoustics) ; Spectroscopy ; Absorption spectroscopy ; Optical filter Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis ; Analytical Chemistry and Sensors
Authors: Max Kerr Winter | Liesbeth M. C. Janssen
Source: Physical Review Research (article, 2025-04-03)
Field: Materials Science — Materials Chemistry

Glassy dynamics in deep neural networks: A structural comparison Deep neural networks (DNNs) share important similarities with structural glasses. Both have many degrees of freedom and their dynamics are governed by a high-dimensional, nonconvex landscape representing either the loss or energy, respectively. Furthermore, both experience gradient descent dynamics subject to noise. In this work we investigate, by performing quantitative measurements on realistic networks trained on the MNIST and CIFAR-10 data sets, the extent to which this qualitative similarity gives rise to glasslike dynamics in neural networks. We demonstrate the existence of a topology trivialization transition as well as the previously studied under-to-overparametrized transition analogous to jamming. By training DNNs with overdamped Langevin dynamics in the resulting disordered phases, we do not observe diverging relaxation times at nonzero temperature nor do we observe any caging effects, in contrast to glass phenomenology. However, the weight overlap function follows a power law in time, with exponent <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mo>≈</a:mo><a:mo>−</a:mo><a:mn>0.5</a:mn></a:mrow></a:math>, in agreement with the mode-coupling theory of structural glasses. In addition, the DNN dynamics obey a form of time-temperature superposition. Finally, dynamic heterogeneity and aging are observed at low temperatures. These results highlight important and surprising points of both difference and agreement between the behavior of DNNs and structural glasses. Dynamics (music) ; Artificial neural network ; Artificial intelligence ; Deep neural networks ; Computer science ; Statistical physics ; Psychology ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Advanced Neuroimaging Techniques and Applications
Authors: Saima Riasat | Chemseddine Maatki | Aneesa Kousar | Muhammad Bilal | Bilel Hadrich | Badr M. Alshammari | Lioua Kolsi
Source: Scientific Reports (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Electrical double layer induced zeta potential effect in a disk cone system with surface catalyzed reaction The present study highlights the zeta potential effect of electrokinetic flow in a disk cone system. The study sheds light on the shape of nanoparticles on ternary nanofluid flow with surface catalyzed chemical reaction. The potential implication is optimization of microfluidic appliances and reactors. The pace of reaction on the surface is analyzed by homogeneous and heterogeneous reactions in the presence of surface catalyzed chemical reactions. The designed model is a system of coupled partial differential equations. Utilization of self-similar variables along with normalization converted the system of partial differential equations into ordinary differential equations. Fourth order collocation method by using MATLB helped to analyze the results in graphical form. It has been concluded that electric field parameter, electroosmotic parameter and zeta potential amplifies the radial velocity while disk cone system is rotating in same direction. The lamina shape of the particles is more sensitive to the temperature variation by increasing thermal radiation parameter as compared to spherical shape. Zeta potential ; Layer (electronics) ; Cone (formal languages) ; Catalysis ; Double layer (biology) ; Materials science ; Biophysics ; Chemical engineering ; Chemistry ; Computer science ; Nanotechnology ; Biology ; Biochemistry ; Algorithm Chemical and Physical Properties of Materials ; Spectroscopy and Quantum Chemical Studies ; Electrostatics and Colloid Interactions
Authors: Daniil E. Smirnov | Oleg S. Morozov | Ekaterina S. Afanasyeva | Viktor V. Avdeev
Source: Acta Crystallographica Section E Crystallographic Communications (article, 2025-01-17)
Field: Chemistry — Organic Chemistry

Synthesis and crystal structures of 4,4′-methylenebis(2,6-diethylaniline) and 4,4′-methylenebis(3-chloro-2,6-diethylaniline) The title compounds 4,4'-methyl-enebis(2,6-di-ethyl-aniline) (C<sub>21</sub>H<sub>30</sub>N<sub>2</sub>, <b>1</b>) and 4,4'-methyl-enebis(3-chloro-2,6-di-ethyl-aniline) (C<sub>21</sub>H<sub>28</sub>Cl<sub>2</sub>N<sub>2</sub>, <b>2</b>) are of significant inter-est as curing agents for a wide range of resins and as building blocks for sterically demanding compounds in the synthesis of ligands for catalysis. This paper describes their synthesis and the preparation of single crystals, with their structures determined through single-crystal X-ray analysis. The presence of the chlorine substituent slightly affects the twist angle between the two aromatic components. The mol-ecules of compound <b>1</b> form a network structure through inter-molecular N-H⋯N bonds and C-H⋯π inter-actions, while in the crystal structure of compound <b>2</b>, the mol-ecules are assembled solely through N-H⋯π inter-actions. Consequently, despite their chemical similarity, it is the precise structural data that enables us to explain their differing reactivity and opens up the possibility of evaluating steric properties for the development of new materials and ligands. Chemistry ; Steric effects ; Crystal structure ; Intermolecular force ; Substituent ; Molecule ; Reactivity (psychology) ; Crystallography ; Stereochemistry ; Organic chemistry Organometallic Compounds Synthesis and Characterization ; Chemical Synthesis and Analysis ; Chemical Synthesis and Reactions
Authors: Xiaoxu Zhang | Chengguo Liu | Xiaoqin Yang | Qian Jiang | Can Liu | Ping Zhao | Jingyan Liu
Source: Materials (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Carbon Dots Synergizing with Phosphate Starch to Construct Dual Conduction Pathways for High-Performance Smart Packaging Films The advancement of smart packaging technology demands high-performance and sustainable sensing materials. While starch is a biodegradable natural polymer, its inherent high crystallinity restricts charge transport capability. This study developed a novel smart sensing film by incorporating ellagic acid-derived blue, fluorescent carbon dots (CDs) into phosphate starch (PS), which is rich in phosphorus. The effects of silver ions (Ag<sup>+</sup>), sodium carboxymethyl cellulose (CMC), and CDs on the film properties were systematically investigated. Results indicate that CDs act as flexible nano-crosslinkers, forming hydrogen bonds with PS molecular chains and effectively balancing strength and toughness-achieving a tensile strength of 5.1 MPa and an elongation at break of 24.1%. Phosphorus, in synergy with CDs, facilitates an efficient dual conduction pathway for ions and electrons: phosphate groups enable ion transport, while the conjugated carbon cores of the CDs provide electron transport channels. This synergistic effect significantly reduces the film's electrical impedance from 6.93 × 10<sup>6</sup> Ω to 1.12 × 10<sup>6</sup> Ω (a reduction of 84%) and enhances thermal stability, increasing the char residue from 1.1% to 18.3%. The PS/CDs composite film exhibits a strong linear current response to pH in the range of 2-7 (<i>R</i><sup>2</sup> = 0.9450), and shows enhanced discrimination between fresh orange juice (pH = 3.38) and spoiled orange juice (pH = 2.68), with a current change of 0.62 × 10<sup>-5</sup> A. Moreover, the film exhibits strong blue fluorescence at 427 nm, with an intensity that shows a pronounced pH-dependent response. This study elucidates the mechanism by which phosphorus and CDs synergistically enhance the sensing performance of starch-based films, offering a new strategy for developing high-performance starch-based materials for smart packaging. Crystallinity ; Composite number ; Starch ; Active packaging ; Phosphate ; Ultimate tensile strength ; Ion Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Nanocomposite Films for Food Packaging
Authors: Ozren Jović
Source: Food Chemistry (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Conformal predictors in chemometric study of mid-infrared food adulteration: quantification of prediction uncertainty This study introduces a novel application of Conformal Predictor Regression to mid-infrared spectroscopic datasets of adulterated foods. Two datasets were analyzed, and 12 high-accuracy underlying models were tested using normalized inductive nonconformity functions. In all cases, significantly lower margins of error, on average 32.1 % lower, were obtained with normalized nonconformity functions compared to absolute residuals at a 99 % confidence level, without any loss of validity. A significant positive correlation (p < 0.05) was found between the generated error margins and the underlying model's accuracy for all studied cases, and this correlation was independent of the set confidence level. Based on the findings of this study, it is recommended to use a robust solution involving multiple combined normalized Conformal Predictors, with optimal efficiency selected in each case, for quantitative determinations of adulteration using mid-infrared data. Conformal predictors can serve as quantitative estimators of accuracy in vibrational spectroscopy for every individual test sample. Chemometrics ; Chemistry ; Environmental chemistry ; Food science ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Rasha A. Youness | Mohammed A. Taha | Essam B. Moustafa | R. Awad | Hossameldin Hussein
Source: Silicon (article, 2025-08-19)
Field: Materials Science — Ceramics and Composites

A Multi-Layered Approach to High-Performance Al-Based FGCs: Investigating the Role of Mono and Hybrid Si3N4 and Granite Reinforcements Abstract This research utilized powder metallurgy technology to create three layers of functionally graded composites (FGCs) based on aluminum alloy (Al6061). It is enhanced with mono and hybrid ceramics, specifically silicon nitride (Si 3 N 4 ) and granite waste. Transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM) were employed to examine the microstructure of the produced FGC powder layers and the sintered samples. Subsequently, the sintered composites' physical, thermal, tribological, and mechanical properties were investigated. The results indicated that incorporating mono and hybrid ceramics positively influenced the refinement of milled powder, reducing the particle size of the Al6061 alloy from 79.18 nm to 36.43 nm, which represents a decrease of approximately 54.39% after reinforcement with 2.5% granite + 5% Si 3 N 4 . Furthermore, both mechanical and tribological properties showed significant improvement. The highly reinforced sample (FGC6), which comprised 1.25% granite + 2.5% Si 3 N 4 in the second layer and 2.5% granite + 5% Si 3 N 4 in the third layer, the compressive strength, Young's modulus, and wear rate improved by about 26.13%, 13.66%, and 16.19%, respectively, compared to the sample without reinforcements (FGC1). Notably, the coefficient of thermal expansion (CTE) for FGC6 samples decreased to 15.1 × 10 –6 /°C, an increase of approximately 35.15% compared to the FGC1 sample (23.3 × 10 –6 /°C). Finally, the addition of reinforcements to the layers harmed the thermal conductivity of the FGC layers. Materials science ; Reinforcement ; Composite material Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Magnesium Oxide Properties and Applications
Authors: Pedro Rodrigues | Clara Sousa
Source: Molecules (article, 2025-01-25)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Spectral Analyses for Camellia japonica Cultivar Identification <i>Camellia japonica</i> is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, <i>C. japonica</i> has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mandatory. Infrared spectroscopy is currently recognized as an accurate and rapid technique for species and/or subspecies identifications, including in plants. However, selecting proper analysis tools (spectra pre-processing, feature selection, and chemometric models) highly impacts the accuracy of such identifications. This study tests the impact of two distinct machine learning-based approaches for discriminating <i>C. japonica</i> cultivars using near-infrared (NIR) and Fourier transform infrared (FTIR) spectroscopies. Leaves infrared spectra (NIR-obtained in a previous study; FTIR-obtained herein) of 15 different <i>C. japonica</i> cultivars (38 plants) were modeled and analyzed via different machine learning-based approaches (Approach 1 and Approach 2), each combining a feature selection method plus a classifier application. Regarding Approach 1, NIR spectroscopy emerged as the most effective technique for predicting <i>C. japonica</i> cultivars, achieving 81.3% correct cultivar assignments. However, Approach 2 obtained the best results with FTIR spectroscopy data, achieving a perfect 100.0% accuracy in cultivar assignments. When comparing both approaches, Approach 2 also improved the results for NIR data, increasing the correct cultivar predictions by nearly 13%. The results obtained in this study highlight the importance of chemometric tools in analyzing infrared data. The choice of a specific data analysis approach significantly affects the accuracy of the technique. Moreover, the same approach can have varying impacts on different techniques. Therefore, it is not feasible to establish a universal data analysis approach, even for very similar datasets from comparable analytical techniques. Cultivar ; Feature selection ; Japonica ; Artificial intelligence ; Camellia ; Fourier transform infrared spectroscopy ; Pattern recognition (psychology) ; Biological system ; Fourier transform ; Mathematics ; Computer science ; Botany ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Qiaoyan Wei | Liangdong Ye | Dacheng Li | Yanyou Huang | Yuling Lv | Ruibing Shen | Ziwei Li | Wenda Zhang | Zuocai Zhang | Yaping Du | Shaorong Lu
Source: Nano Research (article, 2025-07-18)
Field: Materials Science — Biomaterials

Multifunctional bagasse nanocellulose-based composite films for intelligent packaging and physiology signal monitoring Metabolism, transpiration, and invasion of pathogens during the storage and transportation of fruits can lead to significant waste and even food safety issues. Therefore, real-time, rapid, and accurate non-destructive monitoring of physiological information during the storage of fruits and vegetables to assess fruit freshness is crucial. Herein, we engineered a degradable and multifunctional humidity sensing film for monitoring fruit freshness. The film is fabricated through the co-assembly of bagasse cellulose nanocrystals (CNC), okra polysaccharides (OPs), silver nanowires (Ag NWs), and phytic acid (PA), utilizing dynamic hydrogen and phosphate bonds. This innovative design endows the CNC/OPs/PA/Ag NWs (COPA) composite film with outstanding mechanical properties, water resistance, low water vapor permeability, antibacterial, degradability, and moisture-sensing ability. Notably, the proposed COPA humidity sensor exhibits high linearity (<i>R</i><sup>2</sup> = 0.994), ultralow hysteresis (1.24%), and 32 days of operational stability across a 35%–98% relative humidity (RH) range, enabling precise freshness monitoring during fruit storage. Significantly, the COPA film prolonged the shelf-life of packaged fruit when compared to conventional PE film packaging. This research establishes a foundational framework for next-generation smart sensors in food quality management and biomedical monitoring applications. Nanocellulose ; Composite number ; Bagasse ; Materials science ; SIGNAL (programming language) ; Nanotechnology ; Composite material ; Cellulose ; Computer science ; Chemical engineering ; Pulp and paper industry ; Engineering Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies ; Material Properties and Processing