jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shunfan Hu | Jianming Ding | Yan Dong | Tianlong Zhang | Hongsheng Tang | Hua Li
Source: RSC Advances (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy Driven by the "double carbon" strategy, petroleum coke short-term demand is growing rapidly as a negative electrode material for artificial graphite. The analysis of petroleum coke physicochemical properties has always been an important part of its research, encompassing significant indicators such as ash content, volatile matter and calorific value. A strategy based on laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics is proposed to realize the rapid and accurate quantification of the above properties. LIBS spectra of 46 petroleum coke samples were collected, and an original random forest (RF) calibration model was constructed by optimizing the pretreatment parameters. The RF calibration model was further optimized based on variable importance measures (VIM) and variable importance in projection (VIP) methods. After variable selection, the elemental spectral lines related to ash content, volatile matter and calorific value modeling were screened out, thus initially exploring the correlation between these properties and elements. Under the optimized spectral pretreatment method, VI threshold and model parameters, the mean relative error (MRE<sub>P</sub>) of the prediction set of ash content, volatile matter and calorific value were 0.0881, 0.0527 and 0.006, the root mean square error (RMSE<sub>P</sub>) of the prediction set of ash content, volatile matter and calorific value were 0.0471%, 0.6178% and 0.2697 MJ kg<sup>-1</sup>, respectively, and the determination coefficient (<i>R</i><sub>P</sub><sup>2</sup>) of the prediction set was 0.9187, 0.9820 and 0.9510, respectively. The combination of LIBS technology and chemometric methods can provide powerful technical means for the analysis and evaluation of the physicochemical properties of petroleum coke. Selection (genetic algorithm) ; Laser-induced breakdown spectroscopy ; Random forest ; Petroleum ; Variable (mathematics) ; Coke ; Environmental science ; Spectroscopy ; Chemistry ; Biochemical engineering ; Computer science ; Engineering ; Mathematics ; Organic chemistry ; Physics ; Machine learning Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Laser-induced spectroscopy and plasma
Authors: Fan Zhang | Naoaki Ono | Shigehiko Kanaya
Source: Molecular Informatics (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Interpret Gaussian Process Models by Using Integrated Gradients Gaussian process regression (GPR) is a nonparametric probabilistic model capable of computing not only the predicted mean but also the predicted standard deviation, which represents the confidence level of predictions. It offers great flexibility as it can be non-linearized by designing the kernel function, made robust against outliers by altering the likelihood function, and extended to classification models. Recently, models combining deep learning with GPR, such as Deep Kernel Learning GPR, have been proposed and reported to achieve higher accuracy than GPR. However, due to its nonparametric nature, GPR is challenging to interpret. While Explainable AI (XAI) methods like LIME or kernel SHAP can interpret the predicted mean, interpreting the predicted standard deviation remains difficult. In this study, we propose a novel method to interpret the prediction of GPR by evaluating the importance of explanatory variables. We have incorporated the GPR model with the Integrated Gradients (IG) method to assess the contribution of each feature to the prediction. By evaluating the standard deviation of the posterior distribution, we show that the IG approach provides a detailed decomposition of the predictive uncertainty, attributing it to the uncertainty in individual feature contributions. This methodology not only highlights the variables that are most influential in the prediction but also provides insights into the reliability of the model by quantifying the uncertainty associated with each feature. Through this, we can obtain a deeper understanding of the model's behavior and foster trust in its predictions, especially in domains where interpretability is as crucial as accuracy. Interpretability ; Ground-penetrating radar ; Kriging ; Outlier ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Machine learning ; Kernel (algebra) ; Gaussian process ; Standard deviation ; Feature selection ; Probabilistic logic ; Nonparametric statistics ; Data mining ; Gaussian function ; Pattern recognition (psychology) ; Gaussian ; Econometrics ; Mathematics ; Statistics ; Radar Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning and Data Classification
Authors: Adrien Donatini | Luiz Pereira | Donald B. Dingwell | Kai‐Uwe Hess | Dirk Müller | Laurent Cormier | Daniel R. Neuville
Source: Journal of the American Ceramic Society (article, 2024-08-29)
Field: Materials Science — Ceramics and Composites

Influence of cerium addition and redox state on silicate structure and viscosity Abstract The viscosities of Ce‐free and Ce‐bearing (∼1.3 mol%, ∼6.5 wt.% Ce 2 O 3 ) soda lime silicate (window glass) melts were measured with respect to oxidation state. Experiments were performed isothermally using a concentric‐cylinder viscometer on melts equilibrated with successively reducing CO–CO 2 gas mixtures within a gas tight vertical tube furnace at 1 atm. Viscosity measurement and sampling were performed at the end of each melt reduction step. Further, viscosities in the glass transition temperature range were estimated using the shift factor method applied to glass transition temperature values determined using differential scanning calorimetry (DSC) measurements on quenched glasses. The Ce speciation at each stepwise melt reduction was probed using Ce L 3 ‐edge X‐ray absorption near‐edge structure (XANES) spectroscopy, while structural information upon Ce addition and reduction was provided by Raman spectroscopy. The viscosities of these materials remain constant at this level of Ce addition and do not vary significantly with redox state at high temperature. Conversely, viscosity values in the glass transition temperature range increase upon both Ce addition and reduction. Our analysis, based on the glass composition analyses obtained via electron probe microanalyzer (EPMA), viscosity calculations, and observations of silicate structural changes, leads to the conclusion that the observed viscosity increase around the glass transition temperature is explained by the high ionic field strength of Ce ions as well as the polymerization behavior of the silicate matrix occurring during reduction of Ce. Because of its low concentration, resulting from its low solubility, Ce redox changes exert only minimal effects on the viscosity of this melt. Analytical Chemistry (journal) ; Glass transition ; Viscometer ; Silicate ; Chemistry ; Viscosity ; Raman spectroscopy ; Differential scanning calorimetry ; XANES ; Materials science ; Thermodynamics ; Spectroscopy ; Polymer ; Composite material Glass properties and applications ; Cultural Heritage Materials Analysis ; Material Dynamics and Properties
Authors: Mostafa Y. Ismail | Juho Antti Sirviö | Veli‐Pekka Ronkainen | Minna Patanen | Ville Karvonen | Henrikki Liimatainen
Source: Cellulose (article, 2024-04-25)
Field: Materials Science — Biomaterials

Delignification of wood fibers using a eutectic carvacrol–methanesulfonic acid mixture analyses of the structure and fractional distribution of lignin, cellulose, and hemicellulose Abstract Delignification and fractional pretreatments are essential for valorization of wood biomass in various bioproducts. Herein, lignocellulose wood fibers were exposed to a eutectic mixture (EM) of carvacrol and methanesulfonic acid for different times. The resulting structural and chemical alterations in biomass were explored in terms of the fiber morphology and fractional chemical composition through fiber image analysis, field-emission scanning electron microscopy, high-performance liquid chromatography, and a novel approach based on fluorescence-lifetime imaging microscopy (FLIM). The autofluorescence of the lignocellulose fibers, which was primarily due to lignin with contributions from cellulose and hemicellulose, enabled application of FLIM in lignocellulose compositional analysis in micro-scale. FLIM analysis revealed that EM treatment efficiently removed lignin from the outer fiber layers. Furthermore, the effective EM treatment time was 3 h (with a residual lignin content of ~ 7 wt%), after which defects were observed on the fibers and the cellulose chains started breaking. This degradation was also indicated by a shift of the lifetime spectra toward the fluorescence lifetime of cellulose with increasing treatment time. Overall, our findings provide valuable insights to the response of lignocellulose fibers to EM treatment, contributing to the important goal of wood biomass application in bioproducts. Lignin ; Hemicellulose ; Cellulose ; Materials science ; Fiber ; Cellulose fiber ; Chemical engineering ; Bioproducts ; Biomass (ecology) ; Lignocellulosic biomass ; Chemistry ; Organic chemistry ; Composite material ; Waste management ; Biofuel ; Agronomy Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Biofuel production and bioconversion
Authors: Olga S. Arvaniti | Efthymios Rodias | Αντωνία Τέρπου | Nikolaos A. Afratis | Gina Athanasiou | Theodοre Zahariadis
Source: Agronomy (review, 2024-11-01)
Field: Chemistry — Organic Chemistry

Bactrocera oleae Control and Smart Farming Technologies for Olive Orchards in the Context of Optimal Olive Oil Quality: A Review Olive oil production is among the most significant pillars of crop production, especially in the Mediterranean region. The management risks undertaken throughout the olive oil production chain can be minimized using smart tools and applications. This review addressed the influence of the fruit fly of Bactrocera oleae (B. oleae) or Dacus oleae on the quality and antioxidant activity of the olives and their products based on the most recent literature data. Furthermore, in this review, we focused on the latest research achievements in remote sensor systems, features, and monitoring algorithms applied to remotely monitor plant diseases and pests, which are summarized here. Thus, this paper illustrates how precision agriculture technologies can be used to help agricultural decision-makers and to monitor problems associated with integrated pest management for crops and livestock, achieving agricultural sustainability. Moreover, challenges and potential future perspectives for the widespread adoption of these innovative technologies are discussed. Olive oil ; Bactrocera ; Context (archaeology) ; Agriculture ; Olea ; Agronomy ; Horticulture ; Biology ; Food science ; Tephritidae ; PEST analysis ; Ecology Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management
Authors: Tanveer Ahmed | Hao‐Chung Kuo | Der‐Hsien Lien
Source: Advanced Photonics Research (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Tailoring Electronic Properties of Colloidal Quantum Dots for Efficient Optoelectronics Colloidal quantum dots (CQDs) are nanocrystals synthesized in solution, boasting remarkable optical properties and notable electronic characteristics, such as size‐tunable bandgaps and high photoluminescence quantum yield. These features, coupled with solution processability, position CQDs as potential candidates for cost‐effective and high‐performance optoelectronic devices. However, several technological challenges hinder the full exploitation of CQDs in optoelectronics. Among these is the need for long insulating organic ligands in liquid‐phase synthesis, which restrict efficient charge injection and transport in quantum dot (QD) films. Furthermore, the high surface‐to‐volume ratios and core–shell structures prompt complexities in terms of doping and modifying electronic properties. The colloidal nature of quantum dots (QDs) also raises challenges regarding controlled deposition and patterning, which are critical for device fabrication. In this review, the imperative is outlined to tailor CQDs for optoelectronic applications, the limitations that obstruct the implementation of desired modifications are elaborated on, and the specific hurdles confronting electronic coupling, targeted doping, and precision patterning of CQDs are focused on. Additionally, herein, a summary of the solutions proposed to date is offered, insights are shared on the discussed topics, and areas warranting future investigation are highlighted. Quantum dot ; Materials science ; Nanotechnology ; Fabrication ; Nanocrystal ; Photoluminescence ; Doping ; Optoelectronics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: N. F. Martins | Marcos Viana | Bernard Maigret
Source: Molecules (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Fungi Tryptophan Synthases: What Is the Role of the Linker Connecting the α and β Structural Domains in Hemileia vastatrix TRPS? A Molecular Dynamics Investigation Tryptophan synthase (TRPS) is a complex enzyme responsible for tryptophan biosynthesis. It occurs in bacteria, plants, and fungi as an αββα heterotetramer. Although encoded by independent genes in bacteria and plants, in fungi, TRPS is generated by a single gene that concurrently expresses the α and β entities, which are linked by an elongated peculiar segment. We conducted 1 µs all-atom molecular dynamics simulations on <i>Hemileia vastatrix</i> TRPS to address two questions: (i) the role of the linker segment and (ii) the comparative mode of action. Since there is not an experimental structure, we started our simulations with homology modeling. Based on the results, it seems that TRPS makes use of an already-existing tunnel that can spontaneously move the indole moiety from the α catalytic pocket to the β one. Such behavior was completely disrupted in the simulation without the linker. In light of these results and the αβ dimer's low stability, the full-working TRPS single genes might be the result of a particular evolution. Considering the significant losses that <i>Hemileia vastatrix</i> causes to coffee plantations, our next course of action will be to use the TRPS to look for substances that can block tryptophan production and therefore control the disease. Tryptophan ; Biology ; Linker ; Gene ; Biochemistry ; Amino acid Enzyme Structure and Function ; Biochemical and Molecular Research ; Polyamine Metabolism and Applications
Authors: Chloé Geoffroy | Romain Berraud‐Pache | Nicolas Chéron | Isabelle McCort‐Tranchepain | Julia Doria | Pierre Paoletti | Laétitia Mony
Source: ACS Chemical Neuroscience (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Reversible Control of Native GluN2B-Containing NMDA Receptors with Visible Light NMDA receptors (NMDARs) are glutamate-gated ion channels playing a central role in synaptic transmission and plasticity. NMDAR dysregulation is linked to various neuropsychiatric disorders. This is particularly true for GluN2B-containing NMDARs (GluN2B-NMDARs), which have major pro-cognitive, but also pro-excitotoxic roles, although their exact involvement in these processes remains debated. Traditional GluN2B-selective antagonists suffer from slow and irreversible effects, limiting their use in native tissues. We therefore developed OptoNAM-3, a photoswitchable negative allosteric modulator selective for GluN2B-NMDARs. OptoNAM-3 provided light-induced reversible inhibition of GluN2B-NMDAR activity with precise temporal control both in vitro and in vivo on the behavior of freely moving <i>Xenopus</i> tadpoles. When bound to GluN2B-NMDARs, OptoNAM-3 displayed remarkable red-shifting of its photoswitching properties allowing the use of blue light instead of UV light to turn-off its activity, which we attributed to geometric constraints imposed by the binding site onto the azobenzene moiety of the ligand. This study therefore highlights the importance of the binding site in shaping the photochemical properties of azobenzene-based photoswitches. In addition, by enabling selective, fast, and reversible photocontrol of native GluN2B-NMDARs with in vivo compatible photochemical properties (visible light), OptoNAM-3 should be a useful tool for the investigation of the GluN2B-NMDAR physiology in native tissues. NMDA receptor ; Neuroscience ; Glutamate receptor ; Allosteric regulation ; Neurotransmission ; Allosteric modulator ; Chemistry ; Long-term depression ; Synaptic plasticity ; Ion channel ; Receptor ; Biophysics ; Biology ; AMPA receptor ; Biochemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Neuroscience and Neuropharmacology Research
Authors: Lucía Labrador‐Páez | Alfredo Casasnovas‐Melián | Elena Junquera | Andrés Guerrero‐Martínez | Rubén Ahijado‐Guzmán
Source: Nanoscale (review, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical dark-field spectroscopy of single plasmonic nanoparticles for molecular biosciences An ideal sensor capable of quantifying analytes in minuscule sample volumes represents a significant technological advancement. Plasmonic nanoparticles integrated with optical dark-field spectroscopy have reached this capability, demonstrating versatility and expanding applicability across <i>in vitro</i> and <i>in vivo</i> subjects. This review underscores the applicability of optical dark-field spectroscopy with single plasmonic nanoparticles to elucidate a wide range of biomolecular characteristics, including binding constants, molecular dynamics, distances, and forces, as well as recording cell communication signals. Perspectives highlight the potential for the development of implantable nanosensors for metabolite detection in animal models, illustrating the technique's efficacy without the need for labeling molecules. In summary, this review aims to consolidate knowledge of this adaptable and robust technique for decoding molecular biological phenomena within the nano- and bio-scientific community. Plasmon ; Spectroscopy ; Dark field microscopy ; Nanoparticle ; Materials science ; Nanotechnology ; Plasmonic nanoparticles ; Field (mathematics) ; Optoelectronics ; Optics ; Physics ; Microscopy Gold and Silver Nanoparticles Synthesis and Applications ; Protein Interaction Studies and Fluorescence Analysis ; Plasmonic and Surface Plasmon Research
Authors: Bernd Bayerlein | Jörg Waitelonis | Henk Birkholz | Matthias Jung | Markus Schilling | Philipp von Hartrott | Marian Bruns | Jörg Schaarschmidt | Kristian Beilke | Marcel Mutz | Vincent Nebel | Veit Königer | Lisa Beran | Tobias Kraus | Akhilesh Vyas | Lars Vogt | Moritz Blum | Basil Ell | Ya‐Fan Chen | Tina Waurischk | Akhil Thomas | Ali Riza Durmaz | Sahar Ben Hassine | Carina Fresemann | Gordian Dziwis | Hossein Beygi Nasrabadi | Thomas Hanke | Melissa Telong | Stephan Pirskawetz | Mohamed Kamal | Thomas Bjarsch | Ursula Pähler | Peter Hofmann | Mena Leemhuis | Özgür Lütfü Özçep | Lars‐Peter Meyer | Birgit Skrotzki | Jörg Neugebauer | Wolfgang Wenzel | Harald Sack | Christoph Eberl | Pedro Dolabella Portella | Tilmann Hickel | Lutz Mädler | Peter Gumbsch
Source: Advanced Engineering Materials (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value. Implementation ; Materials science ; Space (punctuation) ; Nanotechnology ; Data science ; Computer science ; Software engineering Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Ivelina Shiskova | Dicho Stratiev | Mariana P. Tavlieva | Angel Nedelchev | Rosen Dinkov | Iliyan Kolev | Frans van den Berg | Simeon Ribagin | Sotir Sotirov | Radoslava Nikolova | Anife Veli | Georgi Georgiev | Krassimir Atanassov
Source: Processes (article, 2024-04-12)
Field: Chemistry — Analytical Chemistry

Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility The compatibility of constituents making up a petroleum fluid has been recognized as an important factor for trouble-free operations in the petroleum industry. The fouling of equipment and desalting efficiency deteriorations are the results of dealing with incompatible oils. A great number of studies dedicated to oil compatibility have appeared over the years to address this important issue. The full analysis of examined petroleum fluids has not been juxtaposed yet with the compatibility characteristics in published research that could provide an insight into the reasons for the different values of colloidal stability indices. That was the reason for us investigating 48 crude oil samples pertaining to extra light, light, medium, heavy, and extra heavy petroleum crudes, which were examined for their colloidal stability by measuring solvent power and critical solvent power utilizing the n-heptane dilution test performed by using centrifuge. The solubility power of the investigated crude oils varied between 12.5 and 74.7, while the critical solubility power fluctuated between 3.3 and 37.3. True boiling point (TBP) analysis, high-temperature simulation distillation, SARA analysis, viscosity, density and sulfur distribution of narrow petroleum fractions, and vacuum residue characterization (SARA, density, Conradson carbon, asphaltene density) of the investigated oils were performed. All the experimentally determined data in this research were evaluated by intercriteria and regression analyses. Regression and artificial neural network models were developed predicting the critical solubility power with correlation coefficients R of 0.80 and 0.799, respectively. Asphaltene ; Solubility ; Compatibility (geochemistry) ; Petroleum ; Crude oil ; Chemistry ; Pour point ; Environmental science ; Pulp and paper industry ; Petroleum engineering ; Chemical engineering ; Organic chemistry ; Engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques