jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louise Marie Buur | Arthur Declercq | Marina Strobl | Robbin Bouwmeester | Sven Degroeve | Lennart Martens | Viktoria Dorfer | Ralf Gabriels
Source: Journal of Proteome Research (article, 2024-03-16)
Field: Chemistry — Spectroscopy

MS<sup>2</sup>Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0 Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS<sup>2</sup>Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS<sup>2</sup>Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS<sup>2</sup>Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS<sup>2</sup>Rescore offers a powerful solution to boost peptide identifications. MS<sup>2</sup>Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS<sup>2</sup>Rescore to be a valuable tool for the wider proteomics community. MS<sup>2</sup>Rescore is available at https://github.com/compomics/ms2rescore. Modular design ; Python (programming language) ; Computer science ; User Friendly ; Documentation ; Graphical user interface ; Interface (matter) ; Software ; User interface ; Human–computer interaction ; Operating system ; Information retrieval Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Zhiming Guo | Xuan Chen | Yiyin Zhang | Chanjun Sun | Heera Jayan | Usman Majeed | Nicholas J. Watson | Xiaobo Zou
Source: Foods (article, 2024-05-28)
Field: Chemistry — Analytical Chemistry

Dynamic Nondestructive Detection Models of Apple Quality in Critical Harvest Period Based on Near-Infrared Spectroscopy and Intelligent Algorithms Apples are usually bagged during the growing process, which can effectively improve the quality. Establishing an in situ nondestructive testing model for in-tree apples is very important for fruit companies in selecting raw apple materials for valuation. Low-maturity apples and high-maturity apples were acquired separately by a handheld tester for the internal quality assessment of apples developed by our group, and the effects of the two maturity levels on the soluble solids content (SSC) detection of apples were compared. Four feature selection algorithms, like ant colony optimization (ACO), were used to reduce the spectral complexity and improve the apple SSC detection accuracy. The comparison showed that the diffuse reflectance spectra of high-maturity apples better reflected the internal SSC information of the apples. The diffuse reflectance spectra of the high-maturity apples combined with the ACO algorithm achieved the best results for SSC prediction, with a prediction correlation coefficient (Rp) of 0.88, a root mean square error of prediction (RMSEP) of 0.5678 °Brix, and a residual prediction deviation (RPD) value of 2.466. Additionally, the fruit maturity was predicted using PLS-LDA based on color data, achieveing accuracies of 99.03% and 99.35% for low- and high-maturity fruits, respectively. These results suggest that in-tree apple in situ detection has great potential to enable improved robustness and accuracy in modeling apple quality. Mathematics ; Algorithm ; Coefficient of determination ; Near-infrared spectroscopy ; Mean squared error ; Artificial intelligence ; Horticulture ; Computer science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Xue Li | Xueliang Fu | Honghui Li
Source: Sensors (article, 2024-10-12)
Field: Chemistry — Analytical Chemistry

A CARS-SPA-GA Feature Wavelength Selection Method Based on Hyperspectral Imaging with Potato Leaf Disease Classification Early blight and ladybug beetle infestation are important factors threatening potato yields. The current research on disease classification using the spectral differences between the healthy and disease-stressed leaves of plants has achieved good progress in a variety of crops, but less research has been conducted on early blight in potato. This paper proposes a CARS-SPA-GA feature selection method. First, the raw spectral data of potato leaves in the visible/near-infrared light region were preprocessed. Then, the feature wavelengths were selected via competitive adaptive reweighted sampling (CARS) and the successive projection algorithm (SPA), respectively. Then, the two sets of wavelengths were reorganized and duplicates were removed, and secondary feature selection was conducted with genetic algorithm (GA). Finally, the feature wavelengths were fed into different classifiers and the parameters were optimized using a real-coded genetic algorithm (RCGA). The experimental results show that the feature wavelengths selected by the CARS-SPA-GA method accounted only for 9% of the full band, and the classification accuracy of the RCGA-optimized support vector machine (SVM) classification model reached 98.366%. These results show that it is feasible to classify early blight and ladybug beetle infestation in potato using visible/near-infrared spectral data, and the CARS-SPA-GA method can substantially improve the accuracy and detection efficiency of potato pest and disease classification. Feature selection ; Blight ; Hyperspectral imaging ; Feature (linguistics) ; Pattern recognition (psychology) ; Support vector machine ; Artificial intelligence ; Selection (genetic algorithm) ; Genetic algorithm ; Computer science ; Feature vector ; Mathematics ; Agronomy ; Biology ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Muhammad Aqeel | Ahmed Sohaib | Muhammad Iqbal | Syed Sajid Ullah
Source: Journal of Dairy Science (article, 2024-11-08)
Field: Chemistry — Analytical Chemistry

Milk adulteration identification using hyperspectral imaging and machine learning Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, adulterants in milk can result in severe health risks, potentially leading to fatal diseases. Detecting and categorizing milk adulteration is crucial for consumer safety and the dairy industry. This research is divided into 2 breakthroughs, destructive and nondestructive methods. In the destructive method, the Lactoscan system was used for qualitative analysis: SNF, density, fat, lactose, conductivity, solids, protein, temperature, and pH level. The research also examines nondistractive hyperspectral imaging (HSI) through HSI Specim Fx-10 (397-1,003 nm; Specim, Spectral Imaging Ltd., Oulu, Finland) analysis to detect various phases of milk adulteration for accurate and user-friendly imaging-based adulterant detection and categorization. Preprocessing involves radiometric correction, image resizing, region of interest selection for feature extraction, and empirical line method to calculate spectral reflectance signature. Machine learning techniques (logistic regression, decision tree, support vector machine, and linear discriminant analysis [LDA]), are employed, with LDA excelling in adulteration identification by learning the spectral signatures. These algorithms are trained and validated using a developed milk adulteration dataset. Training, testing, and validation accuracy, precision, recall, F<sub>1</sub> score, kappa, and Matthew's correlation coefficient metrics showcase the effectiveness of the proposed pipeline, outclassing numerous state-of-the-art approaches with a validation accuracy of 100%. In conclusion, this study established a multiclass model capable of detecting milk adulterant behavior, showing significant practical application for milk quality assessment. Hyperspectral imaging ; Identification (biology) ; Chromatography ; Chemistry ; Environmental science ; Remote sensing ; Food science ; Artificial intelligence ; Computer science ; Biology ; Geology ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Xinyu Wang | Feng Huang | Qian Hu | Jiawei Zhang | Jing Liu
Source: npj Materials Degradation (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Galvanic corrosion behavior of welded joint in marine atmosphere environment based on capillary microcell Abstract The kinetical behavior and mechanism of micro-galvanic corrosion of Q690 bainitic weathering steel welded joint in marine atmosphere were investigated. The results showed that a micro-galvanic corrosion cell formed among different regions of the Q690 welded joint. The kinetical behavior of micro-galvanic corrosion could be divided into four stages: the initiation and development of micro-galvanic corrosion, the decreasing of galvanic corrosion rate, the polarity reversal of galvanic couple, and the disappearance of galvanic effect. The difference of corrosion rate between Q690 steel and its welded joint is closely related to the micro-galvanic corrosion kinetic behavior. Galvanic cell ; Corrosion ; Galvanic corrosion ; Materials science ; Metallurgy ; Welding ; Galvanic anode ; Microcell ; Joint (building) ; Cathodic protection ; Electrochemistry ; Electrode ; Chemistry ; Structural engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Chanaprom Cholsuk | Ashkan Zand | Aslı Çakan | Tobias Vogl
Source: The Journal of Physical Chemistry C (preprint, 2024-07-17)
Field: Materials Science — Materials Chemistry

The hBN Defects Database: A Theoretical Compilation of Color Centers in Hexagonal Boron Nitride Color centers in hexagonal boron nitride (hBN) have become an intensively researched system due to their potential applications in quantum technologies. There has been a large variety of defects being fabricated, yet, for many of them, the atomic origin remains unclear. The direct imaging of the defect is technically very challenging, in particular since, in a diffraction-limited spot, there are many defects and then one has to identify the one that is optically active. Another approach is to compare the photophysical properties with theoretical simulations and identify which defect has a matching signature. It has been shown that a single property for this is insufficient and causes misassignments. Here, we publish a density functional theory-based searchable online database covering the electronic structure of hBN defects (257 triplet and 211 singlet configurations), as well as their photophysical fingerprint (excited state lifetime, quantum efficiency, transition dipole moment and orientation, polarization visibility, and many more). All data is open-source and publicly accessible at https://h-bn.info and can be downloaded. It is possible to enter the experimentally observed defect signature and the database will output possible candidates which can be narrowed down by entering as many observed properties as possible. The database will be continuously updated with more defects and new photophysical properties (which can also be specifically requested by any users). The database therefore allows one to reliably identify defects but also investigate which defects might be promising for magnetic field sensing or quantum memory applications. Hexagonal boron nitride ; Hexagonal crystal system ; Materials science ; Boron nitride ; Boron ; Nitride ; Crystallography ; Nanotechnology ; Chemistry ; Physics ; Nuclear physics ; Layer (electronics) Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; Semiconductor materials and devices
Authors: Arindam Bala | Mayuri Sritharan | Na Liu | Muhammad Naqi | Anamika Sen | Gyuchull Han | Hyun Yeol Rho | Youngki Yoon | Sunkook Kim
Source: InfoMat (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Active pixel image sensor array for dual vision using large‐area bilayer <scp>WS<sub>2</sub></scp> Abstract Transition metal dichalcogenides (TMDs) are a promising candidate for developing advanced sensors, particularly for day and night vision systems in vehicles, drones, and security surveillance. While traditional systems rely on separate sensors for different lighting conditions, TMDs can absorb light across a broad‐spectrum range. In this study, a dual vision active pixel image sensor array based on bilayer WS 2 phototransistors was implemented. The bilayer WS 2 film was synthesized using a combined process of radio‐frequency sputtering and chemical vapor deposition. The WS 2 ‐based thin‐film transistors (TFTs) exhibit high average mobility, excellent I on / I off , and uniform electrical properties. The optoelectronic properties of the TFTs array exhibited consistent behavior and can detect visible to near‐infrared light with the highest responsivity of 1821 A W −1 (at a wavelength of 405 nm) owing to the photogating effect. Finally, red, green, blue, and near‐infrared image sensing capabilities of active pixel image sensor array utilizing light stencil projection were demonstrated. The proposed image sensor array utilizing WS 2 phototransistors has the potential to revolutionize the field of vision sensing, which could lead to a range of new opportunities in various applications, including night vision, pedestrian detection, various surveillance, and security systems. image Image sensor ; Materials science ; Night vision ; Optoelectronics ; Pixel ; Responsivity ; Visible spectrum ; Photodetector ; Computer science ; Bilayer ; Optics ; Artificial intelligence ; Physics 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Perovskite Materials and Applications
Authors: Zhen Wang | Yantao Yang | Fatemeh Parastesh | Shuai Cao | Jyhpyng Wang
Source: Physica Scripta (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Chaotic dynamics of a carbon nanotube oscillator with symmetry-breaking Abstract Carbon nanotubes (CNTs) are pivotal components in nanoelectromechanical applications. Investgaing the dynamics of CNTs helps in expanding our understanding of their mechanical properties in nanoengineering endeavors. This study delves into the chaotic characteristic of single-walled carbon nanotube (SWCNT) systems involving phase-shifting external excitation and cubic nonlinear damping. Employing compactification theory, we investigate the system’s dynamics at infinity, unveiling its global structure. Analytic solutions for homoclinic/heteroclinic orbits are derived, and utilizing the Melnikov method, we establish criteria for chaos onset in this periodically excited, perturbed SWCNT system. Through bifurcation diagrams, Lyapunov exponent spectra, Poincaré sections, and homoclinic bifurcation surfaces, we empirically confirm the occurrence of chaos, substantiating our theoretical analyses with simulation results. Symmetry breaking ; Chaotic ; Carbon nanotube ; Dynamics (music) ; Symmetry (geometry) ; Spontaneous symmetry breaking ; Physics ; Classical mechanics ; Quantum mechanics ; Materials science ; Nanotechnology ; Computer science ; Mathematics Carbon Nanotubes in Composites ; Mechanical and Optical Resonators ; Nanotechnology research and applications
Authors: Marc-Antoine Gerault | Luc Camoin | Samuel Granjeaud
Source: Bioinformatics Advances (article, 2024-01-01)
Field: Chemistry — Spectroscopy

DIAgui: a Shiny application to process the output from DIA-NN Code and documentation are available on GitHub at https://github.com/marseille-proteomique/DIAgui. The package is written in R and also uses C++ code. A vignette shows its use in an R command line workflow. Workflow ; Computer science ; Documentation ; Programming language ; Visualization ; Software ; Source code ; Code (set theory) ; Process (computing) ; R package ; Software engineering ; Source lines of code ; Data mining ; Computer graphics (images) ; Database Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Yuan Jiang | Jinshan Li | Xiang Lin Yang | Ruihao Yuan
Source: Materials Genome Engineering Advances (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Applications of generative adversarial networks in materials science Abstract Generative adversarial networks (GANs), as a powerful tool for inverse materials discovery, are being increasingly applied in various fields of materials science. This review provides systematic investigations on the applications of GANs from a group of different aspects. The basic principles of GANs are first introduced; then a detailed review of GANs‐based studies regarding distinct scenarios across composition design, processing optimization, crystal structure search, microstructure characterization and defect detection is presented. At the end, several challenges and possible solutions are discussed and outlined. This overview highlights the efficacy of GANs in materials science, and may stimulate the further use of GANs for more intriguing achievements. Adversarial system ; Generative grammar ; Computer science ; Characterization (materials science) ; Generative adversarial network ; Data science ; Management science ; Artificial intelligence ; Nanotechnology ; Deep learning ; Engineering ; Materials science Machine Learning in Materials Science ; Integrated Circuits and Semiconductor Failure Analysis ; Microstructure and mechanical properties
Authors: Hossein Omidian | Renae L. Wilson
Source: Materials (review, 2024-08-07)
Field: Materials Science — Surfaces, Coatings and Films

Polydopamine Applications in Biomedicine and Environmental Science This manuscript explores the multifaceted applications of polydopamine (PDA) across various scientific and industrial domains. It covers the chemical aspects of PDA and its potential in bone tissue engineering, implant enhancements, cancer treatment, and nanotechnology. The manuscript investigates PDA's roles in tissue engineering, cell culture technologies, surface modifications, drug delivery systems, and sensing techniques. Additionally, it highlights PDA's contributions to microfabrication, nanoengineering, and environmental applications. Through detailed testing and assessment, the study identifies limitations in PDA-related research, such as synthesis complexity, incomplete mechanistic understanding, and biocompatibility variability. It also proposes future research directions aimed at improving synthesis techniques, expanding biomedical applications, and enhancing sensing technologies to optimize PDA's efficacy and scalability. Biomedicine ; Nanoengineering ; Nanotechnology ; Microfabrication ; Scalability ; Biocompatibility ; Computer science ; Biochemical engineering ; Systems engineering ; Engineering ; Data science ; Materials science ; Bioinformatics ; Medicine ; Biology Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Dendrimers and Hyperbranched Polymers
Authors: Omid Moradi | Masoud Mirzaian | Sajjad Sedaghat
Source: Scientific Reports (article, 2024-09-27)
Field: Chemistry — Organic Chemistry

Poly(methyl methacrylate) functionalized graphene oxide/CuO as nanocomposite for efficient removal of dye pollutants In this research, the use of a three-component nanocomposite of graphene oxide-methyl methacrylate and copper(II) oxide (PMMA-GO-CuO) was investigated. The aim of synthesizing this nanocomposite is to removal dye pollutants, specifically methylene blue (MB) and methyl orange (MO), which are commonly used in dyeing industries, through adsorption. The study focuses on creating GO-CuO and PMMA-GO-CuO nanocomposites as effective adsorbents. A simple and quick method led to the development of the PMMA-GO-CuO nanocomposite, which shows enhanced physical and chemical properties. Key materials include graphene oxide, methyl methacrylate, and copper(II) oxide nanoparticles. Characterization techniques such as FT-IR, XRD, SEM, and TGA were used to analyze the nanocomposite. Results indicate that dye adsorption is more effective at lower pH levels, suggesting that the PMMA-GO-CuO nanocomposite can efficiently remove dyes from industrial wastewater. The experimental data showed that the Langmuir isotherm model accurately represented the equilibrium adsorption, with maximum capacities of 285.71 mg g<sup>-1</sup> for methylene blue and 256.41 mg g<sup>-1</sup> for methyl orange, indicating a single layer of adsorption. The kinetics followed a pseudo-second order model, suggesting that the adsorption process involves chemical bonding. Additionally, thermodynamic parameters (ΔG°, ΔH°, and ΔS°) indicated that the adsorption is spontaneous. The adsorption mechanism involves hydrogen bonding, π-π interactions, and electrostatic interactions. This study investigates how factors like pH, temperature, contact time, and dye concentration affect the adsorption of methyl orange and methylene blue dyes. A PMMA-GO-CuO nanocomposite was used, achieving 84% removal of MB and 35% removal of MO from industrial wastewater. This study highlights the promising potential of PMMA-GO-CuO nanocomposite as an effective material for the removal of dye pollutants from industrial wastewater. The results showed that the graphene oxide in the composite is effective for removing cationic dyes due to its negative charge. Further research will focus on the optimization of the synthesis process with the aim of achieving competitive performance of this nanocomposite on a large scale. These findings not only advance the field of nanocomposite materials but also provide a practical solution to an important environmental issue, demonstrating the innovation of the present study in the literature. Nanocomposite ; Graphene ; Oxide ; Pollutant ; Methyl methacrylate ; Poly(methyl methacrylate) ; Methacrylate ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Composite material ; Organic chemistry ; Copolymer ; Polymer ; Metallurgy Nanomaterials for catalytic reactions ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal