jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandros Sopasakis | Maria Nilsson | Mattias Askenmo | Fredrik Nyholm | Lillemor Mattsson Hultén | Victoria Rotter Sopasakis
Source: PLoS ONE (article, 2024-04-02)
Field: Chemistry — Spectroscopy

Machine learning evaluation for identification of M-proteins in human serum Serum electrophoresis (SPEP) is a method used to analyze the distribution of the most important proteins in the blood. The major clinical question is the presence of monoclonal fraction(s) of antibodies (M-protein/paraprotein), which is essential for the diagnosis and follow-up of hematological diseases, such as multiple myeloma. Recent studies have shown that machine learning can be used to assess protein electrophoresis by, for example, examining protein glycan patterns to follow up tumor surgery. In this study we compared 26 different decision tree algorithms to identify the presence of M-proteins in human serum by using numerical data from serum protein capillary electrophoresis. For the automated detection and clustering of data, we used an anonymized data set consisting of 67,073 samples. We found five methods with superior ability to detect M-proteins: Extra Trees (ET), Random Forest (RF), Histogram Grading Boosting Regressor (HGBR), Light Gradient Boosting Method (LGBM), and Extreme Gradient Boosting (XGB). Additionally, we implemented a game theoretic approach to disclose which features in the data set that were indicative of the resulting M-protein diagnosis. The results verified the gamma globulin fraction and part of the beta globulin fraction as the most important features of the electrophoresis analysis, thereby further strengthening the reliability of our approach. Finally, we tested the algorithms for classifying the M-protein isotypes, where ET and XGB showed the best performance out of the five algorithms tested. Our results show that serum capillary electrophoresis combined with decision tree algorithms have great potential in the application of rapid and accurate identification of M-proteins. Moreover, these methods would be applicable for a variety of blood analyses, such as hemoglobinopathies, indicating a wide-range diagnostic use. However, for M-protein isotype classification, combining machine learning solutions for numerical data from capillary electrophoresis with gel electrophoresis image data would be most advantageous. Serum protein electrophoresis ; Gradient boosting ; Decision tree ; Random forest ; Artificial intelligence ; Blood proteins ; Myeloma protein ; Gel electrophoresis of proteins ; Cluster analysis ; Computer science ; Capillary electrophoresis ; Machine learning ; Boosting (machine learning) ; Electropherogram ; Multiple myeloma ; Computational biology ; Biology ; Antibody ; Immunology ; Molecular biology ; Polyacrylamide gel electrophoresis ; Biochemistry ; Monoclonal ; Monoclonal antibody Advanced Proteomics Techniques and Applications ; Clinical Laboratory Practices and Quality Control ; Genetics, Bioinformatics, and Biomedical Research
Authors: Heejae Kwon | E. Kim | Sungha Cho Sungha Cho | Deuk-Chul Kwon | Hee-Hwan Choe Hee-Hwan Choe | Minseok Choi Minseok Choi
Source: East Asian Journal on Applied Mathematics (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Plasma-Simulation Physics Informed Neural Networks (PS-PINNs) for Global Discharge Models We consider the global discharge model in plasma simulations using physics-informed neural networks (PINNs). Our method, named Plasma-Simulation PINNs (PS-PINNs), effectively addresses the inherent stiffness and multiphysics aspects of the global model. Logarithmically equidistant points are employed to capture the steep behaviour in state variables during early stages. This distribution, while typical for handling stiffness in standard numerical methods, can hinder neural network (NN) training. To overcome this, we introduce a pre-processing layer featuring logarithmic transformation and standardization, significantly improving neural network training efficiency. In addition, our model addresses the complex interactions between multiple species common in the plasma problem, resulting in numerous physics loss terms. It is important to balance among various loss terms during training. To this end, we employ a self-adaptive loss balanced method to adaptively choose the weights, enhancing training robustness and effectiveness. The effectiveness of the proposed framework is demonstrated through several examples, including the forward and inverse problems in the chlorine global discharge model, and parameter dependency analysis. Artificial neural network ; Plasma ; Statistical physics ; Physics ; Computer science ; Artificial intelligence ; Nuclear physics High voltage insulation and dielectric phenomena ; Plasma Diagnostics and Applications
Authors: D. Surya Prabha | S. Saraswathi | J Lekha | Loghesh VS
Source:  (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Bell Pepper Leaf Disease Classification Using Support Vector Machine Bell Pepper is a plant commonly cultivated in India and a significant exporter to various foreign nations. Bell Pepper is a type of plant that is mainly utilized as a spice. However, bacterial infection occurs while crop cultivation is rapidly increasing, leading to low-quality exports and degraded Support vector machine ; Pepper ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Computer security Spectroscopy and Chemometric Analyses
Authors: Pavel V. Kolesnichenko | Lukas Wittenbecher | Qianhui Zhang | Run Yan Teh | Chandni Babu | Michael S. Fuhrer | Anders Mikkelsen | Donatas Zigmantas
Source: Nano Letters (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Sub-100 fs Formation of Dark Excitons in Monolayer WS<sub>2</sub> Two-dimensional semiconducting transition metal dichalcogenides are promising materials for optoelectronic applications due to their strongly bound excitons. While bright excitons have been thoroughly scrutinized, dark excitons have been much less investigated, as they are not directly observable with far-field spectroscopy. However, with their nonzero momenta, dark excitons are significant for applications requiring long-range transport or coupling to external fields. We access such dark excitons in WS<sub>2</sub> monolayer using transient photoemission electron microscopy with subdiffraction limited spatial resolution (75 nm) and exceptionally high temporal resolution (13 fs). Image time series of the monolayer are recorded at several different fluences. We directly observe the ultrafast formation of dark K-Λ excitons occurring within 14-50 fs and follow their subsequent picosecond decay. We distinguish exciton dynamics between the monolayer's interior and edges and conclude that the picosecond-scale evolution of dark excitations is defect-mediated while intervalley scattering is not affected by the defects. Exciton ; Monolayer ; Materials science ; Physics ; Chemistry ; Condensed matter physics ; Nanotechnology 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques
Authors: David T. S. Perkins | Aires Ferreira
Source: Physical review. B./Physical review. B (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

Ultrafast all-electrical universal nanoqubits We propose how to create, control, and read out real-space localized spin qubits in proximitized finite graphene nanoribbon (GNR) systems using purely electrical methods. Our proposed are formed of in-gap singlet-triplet states that emerge through the interplay of Coulomb and relativistic spin-dependent interactions in GNRs placed on a magnetic substrate. Application of an electric field perpendicular to the GNR heterostructure leads to a sudden change in the proximity couplings, i.e., a quantum quench, which enables us to deterministically rotate the nanoqubit to any arbitrary point on the Bloch sphere. We predict these spin qubits to undergo Rabi oscillations with optimal visibility and frequencies in excess of 10 GHz. Our findings open up an avenue for the realization of graphene-based quantum computing with ultrafast all-electrical methods. Published by the American Physical Society 2024 Ultrashort pulse ; Physics ; Materials science ; Quantum mechanics ; Laser Graphene research and applications ; Quantum and electron transport phenomena ; Quantum-Dot Cellular Automata
Authors: Haiyang Jiang | Yaoyao Cui | Yanguo Jia
Source: IEEE Access (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Edible Oil Identification Technology Based on Three-Dimensional Fluorescence Spectroscopy and MPCA-LDA The issue of edible oil quality is a serious food safety hazard worldwide, posing a direct threat to public health. Based on this, research on edible oil identification technology using three-dimensional fluorescence spectroscopy combined with MPCA-LDA was conducted in this paper. Firstly, the FS920 fluorescence spectrometer was used to determine the three-dimensional fluorescence spectrum data of three types of sesame oil with different qualities. Then, the main mode features of the three-dimensional fluorescence spectrum were extracted using MPCA. Finally, an identification model for experimental samples was established using LDA. The research shows that MPCA-LDA exhibits higher accuracy and sensitivity compared to the NPLS-DA method, enabling it to identify adulterated and counterfeit sesame oil with 100% accuracy. The MPCA-LDA methods can effectively identify adulterated and inferior edible oils, thereby helping to ensure food safety and consumer health. Identification (biology) ; Computer science ; Spectroscopy ; Edible oil ; Artificial intelligence ; Pattern recognition (psychology) ; Fluorescence spectroscopy ; Fluorescence ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Environmental chemistry ; Physics ; Food science ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Rouillon Charlie | Foucat Loïc | Chaunier Laurent | Maigret Jean-Eudes | El Maana Sana | Duchemin Benoit | Cathala Bernard | Villares Ana | Céline Moreau
Source: Cellulose (article, 2024-10-12)
Field: Materials Science — Biomaterials

From water molecule mobility to water-resistance of swollen oriented and non-oriented cellulose nanofibril cryogels Cellulose ; Materials science ; Water resistance ; Molecule ; Chemical engineering ; Composite material ; Polymer chemistry ; Chemistry ; Organic chemistry ; Engineering Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity
Authors: Sara León-Ecay | Ó. López-Campos | Ainara López-Maestresalas | K. Insausti | Bryden Schmidt | N. Prieto
Source: Food Research International (article, 2024-11-13)
Field: Chemistry — Analytical Chemistry

Using portable visible and near-infrared spectroscopy to authenticate beef from grass, barley, and corn-fed cattle Beef cattle ; Agronomy ; Chemistry ; Biology ; Animal science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Pooyan Parnian | Alberto D’Amore
Source: Journal of Composites Science (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

Investigating the Electrical and Mechanical Properties of Polystyrene (PS)/Untreated SWCNT Nanocomposite Films This paper presents a study of the electrical and mechanical properties of polystyrene (PS)/carbon nanotube (CNT) composites prepared using the doctor blade technique. The nanocomposite films of PS/CNT were prepared by casting a composite solution of PS/CNT in tetrahydrofuran (THF) on a glass substrate using a doctor blade and drying in an oven. The nanocomposite films were then characterized using a tensile test and the four-point probe method to evaluate their mechanical properties and electrical conductivity. The experimental results were used to analyze the unpredicted behavior of the nanocomposite films. The experimental results showed that the electrical conductivity of the nanocomposite films became almost insensitive or unmeasurable with increasing CNT content for very dilute PS–THF solutions. In contrast, at higher PS concentrations, film conductivity increased to a given CNT threshold and then decreased. Based on PS–THF viscosity–concentration data, a discussion is elaborated that partially justifies the experimental results. Nanocomposite ; Polystyrene ; Materials science ; Composite material ; Carbon nanotube ; Polymer Carbon Nanotubes in Composites ; Polymer Nanocomposites and Properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Pamela Miśkiewicz | Magdalena Tokarska
Source: Autex Research Journal (article, 2024-01-01)
Field: Materials Science — General Materials Science

Shaping of thermal protective properties of basalt fabric-based composites by direct surface modification using magnetron sputtering technique Abstract A direct modification of the surface of the basalt fabric was carried out by using magnetron sputtering to obtain composites intended for effective protection against contact and radiant heat. One-layer composite with a coating of aluminum (Al) and zirconium dioxide (ZrO 2 ), two-layer composite with a coating of Al/ZrO 2 , and two-layer composite with a coating of Al/(ZrO 2 + titanium dioxide [TiO 2 ]) were deposited on the fabric surface. Scanning electron microscopy with energy-dispersive X-ray spectroscopy analysis was used to assess the coating on basalt fabrics and determine their chemical composition. Parameters such as thermal conductivity coefficient, resistance to radiant heat, and resistance to contact heat for a contact temperature of 250°C were determined for assessment of the composites from the point of view of protective properties. The similarity analysis of composites was performed to state the impact of coating components’ content and coating thickness on chosen parameters. It was found that a two-layer composite in which the outer layer is Al and the inner layer is a mixture of ZrO 2 and TiO 2 provides good thermal insulation properties. The composites capable of protecting against contact heat at the first efficiency level and against radiant heat at the second efficiency level were obtained. Materials science ; Composite material ; Coating ; Composite number ; Layer (electronics) ; Scanning electron microscope ; Sputter deposition ; Thermal conductivity ; Titanium dioxide ; Sputtering ; Thin film Material Properties and Applications ; Recycling and utilization of industrial and municipal waste in materials production ; Flame retardant materials and properties
Authors: Josip Sacher | Nenad Bolf | Marko Sejdić
Source: Crystals (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Batch Cooling Crystallization of a Model System Using Direct Nucleation Control and High-Performance In Situ Microscopy The aim of this study was to investigate the use of automated high performance in situ microscopy (HPM) for monitoring and direct nucleation control (DNC) during cooling crystallization. Compared to other techniques, HPM enables the detection of small crystals in the range of 1 to 10 μm. Therefore, a novel DNC-controlled variable was investigated to determine the potential improvement of the method. The laboratory system and process control software were developed in-house. A well-studied crystallization model system, the seeded batch cooling crystallization of α-glycine from water, was investigated under normal conditions and temperatures below 60 °C. It was postulated that length-weighted edge-to-edge counts in the range of 1 to 10 μm would be most sensitive to the onset of secondary nucleation and are therefore, used as a control variable. Linear cooling experiments were conducted to determine the initial setpoint for the DNC experiments. Three DNC experiments were then performed with different setpoints and an upper and lower counts limit. It was found that the DNC method can be destabilized with a low setpoint and narrow counts limits. In addition, the new controlled variable is highly sensitive to the formation of bubbles at the microscope window and requires careful evaluation. To address the advantages of the DNC method, an additional linear cooling experiment of the same duration was performed, and it was found that the DNC method resulted in a larger average crystal size. Overall, it can be concluded that the HPM method is suitable for DNC control and could be improved by modifying the image processing algorithm. Nucleation ; Crystallization ; In situ ; Materials science ; Microscopy ; Optical microscope ; Chemical engineering ; Thermodynamics ; Chemistry ; Optics ; Physics ; Engineering ; Composite material ; Scanning electron microscope Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions ; Calcium Carbonate Crystallization and Inhibition
Authors: Maorong Zheng | Xinyang He | Wendi Liu | Yunna Hao | Liming Wang | Xiaohong Qin
Source: Nano Research (article, 2024-06-01)
Field: Materials Science — Polymers and Plastics

Dual-mode triboelectric field polarization induced sweat evaporation for personal moisture management cotton textiles Cotton fiber, as a soft and skin-friendly natural fiber, is gaining increasing attention, but how to realize the ideal cotton fabric for sweat rapid evaporation remains challenging. Herein, for the first time, a personal moisture management cotton electronic textile (PMMC e-textile) with a pleated structure and dual-mode triboelectric promoting evaporation is developed. The PMMC e-textile can not only rapidly evaporate sweat in the form of small molecules through electric field polarization, but also stably monitor the movement signal of the human body in the state of sweating. In the two working modes, the water evaporation rate is 0.210 g h<sup>-1</sup> in the triboelectric field generated in the horizontal stretching-recovery mode, and 0.247 g h<sup>-1</sup> in the vertical contact separation mode (1.41 and 1.66 times faster than cotton fabric, respectively). This work exhibits a good fusion of wet comfort textiles and wearable electronics. Triboelectric effect ; Materials science ; Moisture ; Evaporation ; Polarization (electrochemistry) ; Composite material ; Nanotechnology ; Chemistry ; Physics ; Meteorology Textile materials and evaluations ; Nanomaterials and Printing Technologies
Authors: Srinidhi Mula | Joris Bierkens | Louis Vanduyfhuys | Monique A. Van Der Veen
Source: The Journal of Physical Chemistry C (article, 2024-11-12)
Field: Chemistry — Inorganic Chemistry

Long-Range and Coupled Rotor Dynamics in NO<sub>2</sub>-MIL-53(Al) by Classical Molecular Dynamics By tuning the steric environment and free pore space in metal–organic frameworks, a large variety of rotor dynamics of the organic linkers can appear. Nitrofunctionalized MIL-53 is a terephthalate-linker-based MOF that shows coupled rotor dynamics between the neighboring linkers along the pore direction. Here, we use classical molecular dynamics up to 6 × 2 × 2 supercells to investigate the range of the correlated linker dynamics. Interestingly, we observe an PNPNPNPN... conformational arrangement (P = nearly planar and N = nonplanar) for the conformations of the linkers in a row along the pore direction in the MOF. We identified correlated linker dynamics emerging among the direct and next nearest neighboring linkers along the pore. Due to 180° rotational flips of the planar linkers along the pore, (1) a change in the width of librations in their direct neighbors (PN) is observed; (2) intriguingly, their next nearest planar neighbors (PP) rotate between 0° and ±180° to reattain aligned (0°, 0°) or (±180°, ±180°) conformations. The presence of correlated dynamics in such linkers over long-length scales occurring at nanoseconds time scales is desirable for applications like ferroelectric switching or diffusion control via geared linker rotation, and this work provides insight into the design for such applications. Linker ; Molecular dynamics ; Planar ; Dynamics (music) ; Chemical physics ; Rotor (electric) ; Materials science ; Crystallography ; Rotational dynamics ; Steric effects ; Rotation (mathematics) ; Chemistry ; Nanotechnology ; Computational chemistry ; Molecule ; Physics ; Stereochemistry ; Computer science ; Geometry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Supramolecular Self-Assembly in Materials
Authors: Samuel P. Gleason | Matthew R. Carbone | Deyu Lu | Jim Ciston
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about distributions and locations of atoms, their coordination numbers and oxidation states, and the bonding characteristics [1]. However, analysis of XAS/EELS data often relies on matching the spectra of an unknown experimental sample to a series of simulated or experimental spectra of standard samples. Here, this limits analysis throughput and the ability to extract quantitative information from a sample. X-ray absorption spectroscopy ; Spectral line ; Computer science ; Supervised learning ; Artificial intelligence ; Materials science ; Machine learning ; Pattern recognition (psychology) ; Absorption spectroscopy ; Physics ; Optics ; Artificial neural network ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Tobias Pietz | Sukrit Gupta | Christoph N. Schlaffner | Saima Ahmed | Hanno Steen | Bernhard Y. Renard | Katharina Baum
Source: Bioinformatics (article, 2024-06-17)
Field: Chemistry — Spectroscopy

PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks The code is available at https://github.com/DILiS-lab/pepermint. Abundance (ecology) ; Proteomics ; Mass spectrometry ; Missing data ; Quantitative proteomics ; Imputation (statistics) ; Computational biology ; Computer science ; Peptide ; Isobaric labeling ; Data mining ; Chemistry ; Biology ; Chromatography ; Biochemistry ; Machine learning ; Ecology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies