jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kainat Fatima | Mashiul Huq | Slavisa Jovanovic | Philippe Poure
Source: Energy Technology (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Analysis of Energy Harvesting Optimization for Series Connected Hybrid Photovoltaic‐Thermoelectric Systems This study addresses the tracking of the maximal electrical power generated in series‐connected hybrid photovoltaic‐thermoelectric (PV‐TE) energy harvesting systems in low‐power applications. The integration of PV‐TE technologies in a hybrid energy harvesting system enhances electricity generation from sunlight by combining the properties of PV cells with the thermoelectric effect in thermoelectric generators. However, the inherent differences in electrical characteristics between the photovoltaic cell and the thermoelectric generator pose challenges in achieving the maximum power point (MPP) for the overall hybrid system. This work reviews the potential of the “differential power processing” (DPP) approach, as well as the associated architectures, DC–DC converter topologies, and maximum power point tracking (MPPT) algorithms. The final goal of this study is to identify the most suitable solutions for low‐power hybrid PV‐TE systems connected in series, implementing the DPP approach. Thermoelectric generator ; Energy harvesting ; Photovoltaic system ; Maximum power point tracking ; Hybrid system ; Electricity generation ; Generator (circuit theory) ; Electricity ; Power (physics) ; Maximum power principle Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Sisi Zhao | Danqi Zhao | Jiangping Song | Huixia Jia | Xiaohui Zhang | Wenlong Yang | Haiping Wang
Source: Agriculture (article, 2025-04-09)
Field: Chemistry — Analytical Chemistry

Rapid Nondestructive Detection of Welsh Onion, Onion, and Chinese Chives Seeds Based on Hyperspectral Imaging Technology The appearance of Allium L. seeds is very similar, and it is difficult to achieve fast and accurate classification using traditional seed classification methods, which may cause damage to the seeds. Therefore, finding a quick and nondestructive classification method is very important to solve the problem of seed confounding in actual production. In this study, hyperspectral imaging technology was combined with a variety of data preprocessing and classification models to achieve rapid and nondestructive classification of Welsh onion, onion, and Chinese chives seeds. In this paper, 1050 Welsh onion, onion, and Chinese chives seeds were used as materials, and their 400–1000 nm spectral images were collected for processing. Standard Normal Variable (SNV), Multivariate Scattering Correction (MSC), First-order Differential (FD), and Second-order Differential (SD) were used to denoise the spectral data. Then the dimensionality was reduced by Principal Component Analysis (PCA). Four classification models, Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (KNN), were used to classify seeds quickly and accurately. The results show that the prediction accuracies of the Original-PLS-DA model, Original-Linear SVM model, and FD-Linear SVM model are the highest, reaching 98%, while the accuracy, recall rate, and F1 score all reach 96%. This study provides a new idea for rapid and nondestructive classification of Allium L. seeds in practical production. Hyperspectral imaging ; Welsh ; Botany ; Biology ; Horticulture ; Remote sensing ; Geography ; Archaeology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Tatiana M. Shamorkina | Laura Pérez Pañeda | Tereza Kadavá | Douwe Schulte | Patrick Pribil | Sibylle Heidelberger | Allison Michele Narlock-Brand | Steven M. Yannone | Joost Snijder | Albert J. R. Heck
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-29)
Field: Chemistry — Spectroscopy

Deep Coverage and Extended Sequence Reads Obtained with a Single Archaeal Protease Expedite <i>de novo</i> Protein Sequencing by Mass Spectrometry The ability to sequence proteins without reliance on a genomic template defines a critical frontier in modern proteomics. This approach, known as <i>de novo</i> protein sequencing, is essential for applications such as antibody sequencing, microbiome proteomics, and antigen discovery, which require accurate reconstruction of peptide and protein sequences. While trypsin remains the gold-standard protease in proteomics, its restricted cleavage specificity limits peptide diversity. This constraint is especially problematic in antibody sequencing, where the functionally critical regions often lack canonical tryptic sites. As a result, exclusively trypsin-based approaches yield sparse reads, leading to sequence gaps. Multi-protease and hybrid-fragmentation strategies can improve the sequence coverage, but they add complexity, compromise scalability and reproducibility. Here, we explore two HyperThermoacidic Archaeal (HTA)-proteases as single-enzyme solutions for <i>de novo</i> antibody sequencing. Each HTA-protease generated about five times more unique peptide reads than trypsin or chymotrypsin, providing high redundancy across all CDRs. Combined with EAciD fragmentation on a ZenoTOF 7600 system, this approach enabled complete, unambiguous antibody sequencing. <i>De novo</i> analysis using PEAKS/DeepNovo and Stitch showed up to fourfold higher alignment scores and reduced the sequence errors within the HTA-generated data. Additionally, the HTA-EAciD approach offers short digestion times, eliminates extensive cleanup, and enables analysis in a single LC-MS/MS run. This streamlined, single-protease approach delivers therefore performance comparable to multi-enzyme workflows, offering a scalable and efficient strategy for <i>de novo</i> protein sequencing across diverse applications. Computational biology ; Biology ; Proteomics ; Protease ; Deep sequencing ; DNA sequencing ; Sequence motif ; Sequence analysis ; Trypsin ; Genetics ; Biochemistry ; Enzyme ; Genome ; DNA ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: B. I. Kotov | Roman Kalinichenko | С. П. Степаненко | Vasyl Lukach | V. O. Hryshchenko | Alvian Kuzmych | Yurii Pantsyr | Іhor Garasymchuk | Volodymyr Vasylyuk
Source: Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska (article, 2025-09-30)
Field: Materials Science — General Materials Science

Implementation of energy-saving modes for electro-radiation drying of oil-containing material using automation tools For industrial processing of oil-containing plant material, it is necessary to reduce its moisture content to 6–8%. The use of convective, energy-intensive dryers for drying seeds to low moisture levels is inefficient. The aim of the work is to improve the mathematical description of dynamic modes of grain drying in infrared energy supply systems and, based on this, to develop an automatic control system. The main method of researching the static and dynamic characteristics of the grain processing process using infrared radiation is the analytical method followed by experimental verification of the obtained mathematical description. To intensify the process of achieving the desired moisture content of the raw material, it is proposed to use contactless heating with infrared radiation. In this case, the issue of preventing overheating and scorching of the material is resolved by using vibration transportation with particle mixing and automating surface temperature control and its stabilization by adjusting the movement speed. Based on the dependencies, it can be concluded that an increase in the temperature of the grain due to distributed power input enhances the intensity of heating and stabilizes the temperature of the grain material. This leads to a reduction in the time required for the development of moisture gradients within individual grains and speeds up the evaporation of moisture from the surface of the grains. The paper clarifies the mathematical model of thermal processes of infrared processing of oilseeds, on the basis of which an automatic control system was developed for the implementation of energy-saving grain drying modes. The research results showed that the introduction of an automated infrared drying mode allows for a reduction in specific energy consumption by 30-40%. Moisture ; Infrared heater ; Automation ; Water content ; Grain drying ; Process control ; Work (physics) ; Process (computing) ; Raw material ; Overheating (electricity) Material Properties and Applications ; Engineering Technology and Methodologies ; Food Drying and Modeling
Authors: Alec Horrmann | Yash Travadi | Kevin Mallery | Grant Schaap | Kaylee Judith Kamalanathan | Nathaniel Bristow | Catalina Galeano‐Garces | Suk Hyun Bae | Harrison Ball | Alexa Hesch | Sarah Pederson | Badrinath R. Konety | Yuliya Olimpiadi | Justin M. Drake
Source: medRxiv (preprint, 2025-09-24)
Field: Chemistry — Spectroscopy

A Plasma-based Deep Proteomic Platform for Early-Stage Breast Cancer Detection Abstract Despite the widespread use of mammography as the standard of care for breast cancer screening, its accuracy remains limited for select patient populations, such as women with high breast density. Liquid biopsy-based tests offer an accessible complement to conventional screening methods. Here, we conducted a case-control study to develop a plasma-based protein classifier to distinguish between early-stage breast cancer patients and healthy individuals. A total of 335 women, comprising 116 patients with newly diagnosed, treatment naïve breast cancer (Stage 0-2) and 219 healthy controls, had plasma samples collected and processed in a blinded manner using a sample preparation method coupled with semi-quantitative, label-free mass spectrometry (MS)-based analysis. The median number of proteins detected per patient across breast cancer and healthy individuals was 6,991 and 6,818, respectively. A machine learning-based classifier was trained and validated on patient proteome profiles using a leave-one-out cross-validation (LOOCV) approach to identify breast cancer patients. The classifier achieved an AUC of 0.96 (95% CI: 0.93-0.97), with a sensitivity of 86.2% (95% CI: 78.8-91.3%) and a specificity of 90.4% (95% CI: 85.8-93.6%). In breast cancer patients, the classifier retained &gt;85% sensitivity regardless of breast density (low density: 87.2%, high density: 90.2%) at 90% specificity. Our workflow demonstrates the potential of plasma proteomics as a potent diagnostic tool in early-stage breast cancer screening. Breast cancer ; Mammography ; Proteomics ; Classifier (UML) ; Proteome Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Biosensors and Analytical Detection
Authors: Aleksei Shefer | Lyudmila V. Yanshole | Alina Grygor’eva | N. V. Yunusova | И. В. Кондакова | Л. В. Спирина | А. И. Шевела | А. Л. Чернышова | Alexander Romanov | Svetlana Tamkovich
Source: Molecules (article, 2025-10-16)
Field: Chemistry — Spectroscopy

Mitochondrial Proteins as Exosomal Cargo: New Breast Cancer Biomarkers &amp; Crucial Players in Carcinogenesis? Mitochondrial proteins (mtPs) packaged into extracellular vesicles (EVs), particularly exosomes, have emerged as promising biomarkers and functional mediators in cancer biology. In this study, we investigated whether breast cancer (BC)-derived exosomes selectively incorporate mtPs, thereby providing insights into their diagnostic potential and role in tumor progression. Exosomes were isolated from conditioned media of multiple BC cell lines and non-tumorigenic breast epithelial cells using ultracentrifugation and were characterized by transmission electron microscopy and flow cytometry for canonical markers CD9 and CD81. Proteomic profiling by MALDI-TOF mass spectrometry revealed a distinct enrichment of mtPs in exosomes derived exclusively from tumor cells, while non-tumorigenic cells lacked such cargo. Identified proteins were predominantly associated with the oxidative phosphorylation system, with Complex I subunits most frequently detected, whereas Complex II components were entirely absent. These findings highlight a cancer-specific mechanism of exosomal mtP incorporation, potentially reflecting altered energy metabolism and stress responses in malignant cells. We conclude that mtPs in exosomes represent both functional contributors to tumor progression and promising candidates for liquid biopsy-based BC diagnostics. Microvesicles ; Flow cytometry ; Exosome ; Breast cancer ; Extracellular vesicles ; Mitochondrion ; TSG101 ; Proteomics ; Liquid biopsy ; Extracellular Advanced Proteomics Techniques and Applications ; interferon and immune responses ; Redox biology and oxidative stress
Authors: Saber Alfurhud | Udo Schwingenschlögl
Source: Scientific Reports (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Engineering of Janus transition metal dichalcogenide bilayers as absorber materials for solar cells Janus transition metal dichalcogenide bilayers are investigated as potential solar cell materials by first-principles calculations to identify candidates with direct band gap and type-II band alignment. The effects of the interface stacking and interface composition are explored. 11 out of the 20 examined bilayers show promising features and therefore are characterized in terms of the charge transfer, absorption spectrum, and power conversion efficiency. Janus ; Transition metal ; Materials science ; Nanotechnology ; Metal ; Chemistry ; Metallurgy Quantum Dots Synthesis And Properties ; Advanced Nanomaterials in Catalysis ; Perovskite Materials and Applications
Authors: Abdulkadir Yağız
Source: Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi (article, 2025-12-03)
Field: Materials Science — Polymers and Plastics

EFFECT OF DIFFERENT FINISHING PROCESSES ON ABRASION RESISTANCE OF UPHOLSTERY FABRICS PRODUCED FROM POLYESTER AND ACRYLIC CHENILLE YARNS Abrasion resistance is one of the primary performance features of chenille fabrics, which have been widely preferred in upholstery production for many years. Therefore, enhancing abrasion resistance is a critical consideration for fabrics containing chenille yarn, as it significantly impacts their performance in various applications. One effective method of improving the abrasion resistance of chenille fabrics is through the finishing processes applied to upholstery fabrics, which play a crucial role in enhancing their durability and longevity. To achieve this, in our study, fabrics were produced using chenille yarn made from two different raw materials, and four distinct finishing processes were applied to these fabrics to assess their impact on performance. Then, the abrasion resistance of the fabrics was measured according to international standards. The results of the abrasion resistance test, including the pile loss, were comprehensively evaluated through both graphical and statistical analyses. Based on these evaluations, the finishing processes that proved to be most effective in enhancing abrasion resistance were identified, thoroughly examined, and discussed in detail. Abrasion (mechanical) ; Polyester ; Yarn ; Raw material ; Textile Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Research in Cotton Cultivation
Authors: Arash Abyaz | Mohammad Hamed Samimi | Amir Abbas Shayegani Akmal
Source: High Voltage (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Analysing the Impact of Frequency Bands on Partial Discharge Measurement Outcomes in Generators Through Model‐Based Approach ABSTRACT The electromagnetic wave produced by a partial discharge (PD) covers a broad range of frequencies, from a few kHz to several 100 MHz. Because of the nonlinear response of the stator winding, each frequency component of the PD pulse experiences different levels of attenuation as it travels through the winding structure. Consequently, the choice of frequency band for PD measurement has a significant impact on the results obtained from these measurements. The measurement frequency band, along with other complexities in PD pulse propagation, adds to the challenge of interpreting the results of PD testing. Currently, there is no universally accepted threshold for harmful activities associated with PD in generators in any reputable standard. This article investigates the influence of the measurement frequency band on the results obtained from PD testing by utilising a comprehensive finite element model of a stator winding. The research findings indicate that measuring the PD apparent charge at lower frequencies, with a relatively wide bandwidth, provides results that are more closely related to the PD charge at its source. Partial discharge ; Attenuation ; Stator ; Range (aeronautics) ; Pulse (music) ; Frequency band ; Radio spectrum ; System of measurement High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Electrostatic Discharge in Electronics
Authors: Carlos Joshua García Montagut | Rubén Paz | Mario Monzón
Source: Polymers (article, 2025-10-14)
Field: Materials Science — General Materials Science

Development of a Non-Spherical Polymeric Particles Calibration Procedure for Numerical Simulations Based on the Discrete Element Method The manufacturing industry, in general, and the plastic industry, in particular, have been developing new materials and process methods that need a correct study and optimization. Nowadays, the main approach to optimize these processes is using numerical methods and, in the case of particulate materials, the Discrete Elements Method to estimate the particles interactions. But those mathematical models use some parameters that depend on the material and must be calibrated, thus requiring an important computational and experimental cost. In this study, we integrate different speed-up procedures and present a general calibration method of Low-Density Polyethylene particles, to obtain the calibrated solid density and Poisson's ratio of the material, the restitution, static and rolling friction factors in the particle-to-particle and particle-to-wall interactions, and the contact model variables (damping factor, stiffness factor, and energy density). For this calibration, four different tests were carried out, both experimentally and with simulations, obtaining the bulk density, the repose and shear angles, and the dropped powder. All these response variables were compared between simulations and experimental tests, and using genetic algorithms, the input parameters (design variables) were calibrated after 85 iterations, obtaining a Mean Absolute Percentage Error of the response variables lower than 2% compared to the experimental results. Calibration ; Discrete element method ; Stiffness ; Finite element method ; Development (topology) ; Angle of repose ; Process (computing) ; Numerical analysis ; Shear (geology) Material Properties and Applications
Authors: Ainesh Bakshi | John Bostanci | William Kretschmer | Zeph Landau | Jerry Li | Allen Liu | Ryan O'Donnell | Ewin Tang
Source:  (preprint, 2025-06-15)
Field: Materials Science — Materials Chemistry

Learning the Closest Product State We study the problem of finding a (pure) product state with optimal fidelity to an unknown $n$-qubit quantum state $ρ$, given copies of $ρ$. This is a basic instance of a fundamental question in quantum learning: is it possible to efficiently learn a simple approximation to an arbitrary state? We give an algorithm which finds a product state with fidelity $\varepsilon$-close to optimal, using $N = n^{\text{poly}(1/\varepsilon)}$ copies of $ρ$ and $\text{poly}(N)$ classical overhead. We further show that estimating the optimal fidelity is NP-hard for error $\varepsilon = 1/\text{poly}(n)$, showing that the error dependence cannot be significantly improved. For our algorithm, we build a carefully-defined cover over candidate product states, qubit by qubit, and then demonstrate that extending the cover can be reduced to approximate constrained polynomial optimization. For our proof of hardness, we give a formal reduction from polynomial optimization to finding the closest product state. Together, these results demonstrate a fundamental connection between these two seemingly unrelated questions. Building on our general approach, we also develop more efficient algorithms in three simpler settings: when the optimal fidelity exceeds $5/6$; when we restrict ourselves to a discrete class of product states; and when we are allowed to output a matrix product state. Product (mathematics) ; State (computer science) ; Computer science ; Mathematics ; Algorithm ; Geometry Machine Learning in Materials Science
Authors: Jin Dong | Zhanyun Zhu | Xiaoxi Li | Ping Zhou | Desheng Lan | Jianhua Huang
Source: npj Heritage Science (article, 2025-08-28)
Field: Chemistry — Spectroscopy

Comparative analysis and optimization of proteomic information mining approaches for proteinaceous binders in painted artifacts Abstract This study systematically compares the performance of two widely used proteomics software packages, FragPipe (FP) and Proteome Discoverer (PD), in the identification of proteinaceous binders in painted artifacts. While both tools deliver comparable performance in terms of protein identification numbers and accuracy, FP demonstrates a marked advantage in computational efficiency, completing database searches within one minute and achieving a 95.7–96.9% reduction in processing time relative to PD. Conversely, PD exhibits strengths in the nuanced analysis of specific proteins, particularly in complex matrices such as egg white glue and mixed adhesive formulations. As an open-source software freely available for non-commercial use, FP affords both high efficiency and robust accuracy in the analytical characterization of polychrome binders, whereas PD offers complementary value through its enhanced capacity for the detection of low-abundance proteins. These findings advance proteomics in archaeology and heritage conservation, optimizing protein identification strategies for ancient painted artifacts. Computational biology ; Computer science ; Chemistry ; Data science ; Biology Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Viral Infectious Diseases and Gene Expression in Insects