jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shakir Ali | Isha Isha | Young‐Cheol Chang
Source: Preprints.org (review, 2023-11-29)
Field: Materials Science — Biomaterials

Ecotoxicological Impact of Bioplastics Biodegradation: A Comprehensive Review The emergence of bioplastics presents a promising solution to the environmental impact of the plastics industry. Biodegradable plastics are engineered to degrade in aquatic or soil environments. Biodegradable bioplastics can degrade in these conditions, according to sustainability standards, and have a low environmental impact. However, not all bioplastics are completely biodegradable, and some, like petrochemical-based plastics, may contribute to plastic pollution. In this comprehensive review, we identify the biodegradability of significant bioplastics, including polyhydroxyalkanoates (PHA), poly(lactic acid) (PLA), poly(butylene succinate) (PBS), and PBS-co-adipate (PBSA) in different environmental conditions given in the literature. Microbe-based bioplastics made from renewable materials like microalgae provide a sustainable alternative due to their high biodegradability compared to other bioplastics. However, we recognize concerns regarding the ecotoxicological impact of these alternatives on soil and aquatic organisms and recommend future research to observe the ecotoxicological behavior of biodegradation by-products. Bioplastic ; Biodegradation ; Polyhydroxyalkanoates ; Biochemical engineering ; Biodegradable plastic ; Environmental science ; Waste management ; Chemistry ; Engineering ; Biology ; Organic chemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Ali Sharifzadegan | Mohammad Behnamnia | Abolfazl Dehghan Monfared
Source: Scientific Reports (article, 2023-10-28)
Field: Chemistry — Analytical Chemistry

Artificial intelligence-based framework for precise prediction of asphaltene particle aggregation kinetics in petroleum recovery The precipitation and deposition of asphaltene on solid surfaces present a significant challenge throughout all stages of petroleum recovery, from hydrocarbon reservoirs in porous media to wellbore and transfer pipelines. A comprehensive understanding of asphaltene aggregation phenomena is crucial for controlling deposition issues. In addition to experimental studies, accurate prediction of asphaltene aggregation kinetics, which has received less attention in previous research, is essential. This study proposes an artificial intelligence-based framework for precisely predicting asphaltene particle aggregation kinetics. Different techniques were utilized to predict the asphaltene aggregate diameter as a function of pressure, temperature, oil specific gravity, and oil asphaltene content. These methods included the adaptive neuro-fuzzy interference system (ANFIS), radial basis function (RBF) neural network optimized with the Grey Wolf Optimizer (GWO) algorithm, extreme learning machine (ELM), and multi-layer perceptron (MLP) coupled with Bayesian Regularization (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) algorithms. The models were constructed using a series of published data. The results indicate the excellent correlation between predicted and experimental values using various models. However, the GWO-RBF modeling strategy demonstrated the highest accuracy among the developed models, with a determination coefficient, average absolute relative deviation percent, and root mean square error (RMSE) of 0.9993, 1.1326%, and 0.0537, respectively, for the total data. Asphaltene ; Kinetics ; Petroleum ; Particle (ecology) ; Computer science ; Chemistry ; Chemical engineering ; Engineering ; Geology ; Organic chemistry ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Sneha Verma | Nik Khadijah Nik Aznan | Kathryn Garside | Thomas J. Penfold
Source: Chemical Communications (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Uncertainty quantification of spectral predictions using deep neural networks We investigate the performance of uncertainty quantification methods, namely deep ensembles and bootstrap resampling, for deep neural network (DNN) predictions of transition metal K-edge X-ray absorption near-edge structure (XANES) spectra. Bootstrap resampling combined with our multi-layer perceptron (MLP) model provides an accurate assessment of uncertainty with >90% of all predicted spectral intensities falling within ±3<i>σ</i> of the true values for <i>held-out</i> data across the nine first-row transition metal K-edge XANES spectra. Artificial neural network ; Deep neural networks ; Spectral line ; Enhanced Data Rates for GSM Evolution ; Computer science ; Artificial intelligence ; Absorption (acoustics) ; Biological system ; Pattern recognition (psychology) ; Statistical physics ; Physics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging
Authors: Maria C. Cringoli | Silvia Marchesan
Source: Molecules (article, 2023-06-24)
Field: Materials Science — Biomaterials

Cysteine Redox Chemistry in Peptide Self-Assembly to Modulate Hydrogelation Cysteine redox chemistry is widely used in nature to direct protein assembly, and in recent years it has inspired chemists to design self-assembling peptides too. In this concise review, we describe the progress in the field focusing on the recent advancements that make use of Cys thiol-disulfide redox chemistry to modulate hydrogelation of various peptide classes. Chemistry ; Cysteine ; Peptide ; Redox ; Disulfide bond ; Nanotechnology ; Protein chemistry ; Biochemistry ; Combinatorial chemistry ; Biophysics ; Organic chemistry ; Biology ; Materials science Supramolecular Self-Assembly in Materials ; Photochromic and Fluorescence Chemistry ; Polydiacetylene-based materials and applications
Authors: Yuefang Peng | Shenghao Li | Ao Huang | Huazhi Gu | Yongshun Zou | Lvping Fu
Source: Journal of the American Ceramic Society (article, 2023-05-11)
Field: Materials Science — Ceramics and Composites

Effect of slag basicity on Turing pattern corrosion of alumina refractory ceramics in the presence of free radicals Abstract Turing pattern corrosion causes serious damage to alumina ceramics. Therefore, the quaternary slag corrosion of alumina refractory ceramics was studied with different slag basicity by adjusting relative content of MgO and Al 2 O 3 . The results show that reactive oxygen species (ROS) like singlet oxygen and superoxide radicals are generated in the slags. The height of corrosion peak is determined by both the original superoxide radical and the amount of non‐bridging oxygen (NBO) of the slags under the change of basicity. Moreover the influence of free radicals on the corrosion peak is greater than that of NBOs. A free‐radical reaction was proposed as the formation mechanism of the Turing pattern corrosion peaks based on ROS. Corrosion ; Radical ; Oxygen ; Slag (welding) ; Chemistry ; Inorganic chemistry ; Singlet oxygen ; Ceramic ; Materials science ; Metallurgy ; Organic chemistry Advanced ceramic materials synthesis ; Anodic Oxide Films and Nanostructures ; Magnesium Oxide Properties and Applications
Authors: Anastasia Yakusheva | Mohamed A. Aly-Eldeen | Alexander Gusev | Olga V. Zakharova | Денис Кузнецов
Source: Nanomaterials (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

Cyan Fluorescent Carbon Quantum Dots with Amino Derivatives for the Visual Detection of Copper (II) Cations in Sea Water Amino- and carboxyl-functionalized carbon quantum dots (Amino-CQDs) were synthesized through fast and simple microwave treatment of a citric acid, ethylenediamine and ethylenediaminetetraacetic acid (EDTA) mix. The reproducible and stable optical properties from newly synthesized CQD dispersion with a maximum absorbance spectra at 330 nm and the symmetric emission maximum at 470 nm made the Amino-CQDs a promising fluorescence material for analytical applications. The highly aminated and chelate moieties on the CQDs was appropriate for a copper (Cu<sup>2+</sup>) cation sensor in the linear range from 1 × 10<sup>-4</sup> mg/mL to 10 mg/mL with a limit of detection at 0.00036 mg/mL by static fluorescence quenching effects. Furthermore, Amino-CQDs demonstrated stable fluorescence parameters for assays in diluted alkali metal solution (Na<sup>+</sup> and K<sup>+</sup>) and sea water. Finally, a visual sensor, based on Amino-CQDs, was successfully created for the 0.01-100 mg/mL range to produce a colorimetric effect that can be registered by computer vision software (Open CV Python). Fluorescence ; Detection limit ; Ethylenediamine ; Cyan ; Ethylenediaminetetraacetic acid ; Copper ; Absorbance ; Citric acid ; Chemistry ; Chelation ; Indigo carmine ; Materials science ; Analytical Chemistry (journal) ; Nuclear chemistry ; Inorganic chemistry ; Chromatography ; Organic chemistry Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Puneet Mishra | Jun‐Li Xu
Source: Journal of Near Infrared Spectroscopy (article, 2023-05-26)
Field: Chemistry — Analytical Chemistry

Multimodal close range hyperspectral imaging combined with multiblock sequential predictive modelling for fresh produce analysis Multimodal measurements are increasingly becoming common in the domain of spectral sensing and imaging for fresh produce. Often multiple sensors are expected to carry complementary information which allows precise estimation of responses. In this study, a novel case of multimodal hyperspectral imaging is described where two different spectral cameras working in the complementary spectral ranges were integrated into a fully standalone system for spectral imaging for fresh produce analysis. Furthermore, a comparative analysis of different multiblock predictive modelling approaches for fusing data from these two complementary spectral cameras is demonstrated. Both multiblock latent space and multiblock variable selection approaches to identify key variables of interest was examined and compared with the analysis carried out on individual data blocks. Prediction of the soluble solids content in grapes was used to demonstrate the application. The presented approach can increase the applications of multimodal hyperspectral imaging for non-destructive analysis. Hyperspectral imaging ; Computer science ; Spectral imaging ; Artificial intelligence ; Imaging spectroscopy ; Pattern recognition (psychology) ; Range (aeronautics) ; Spectral analysis ; Multispectral image ; Remote sensing ; Computer vision ; Materials science ; Spectroscopy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Stefan B. Lindström | Rabab Amjad | Elin Gåhlin | Linn Andersson | Marcus Kaarto | Kateryna Liubytska | Johan Persson | Jan-Erik Berg | Birgitta A. Engberg | Fritjof Nilsson
Source: Fibers (article, 2023-12-25)
Field: Chemistry — Analytical Chemistry

Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques In the pulp and paper industry, pulp testing is typically a labor-intensive process performed on hand-made laboratory sheets. Online quality control by automated image analysis and machine learning (ML) could provide a consistent, fast and cost-efficient alternative. In this study, four different supervised ML techniques—Lasso regression, support vector machine (SVM), feed-forward neural networks (FFNN), and recurrent neural networks (RNN)—were applied to fiber data obtained from fiber suspension micrographs analyzed by two separate image analysis software. With the built-in software of a commercial fiber analyzer optimized for speed, the maximum accuracy of 81% was achieved using the FFNN algorithm with Yeo–Johnson preprocessing. With an in-house algorithm adapted for ML by an extended set of particle attributes, a maximum accuracy of 96% was achieved with Lasso regression. A parameter capturing the average intensity of the particle in the micrograph, only available from the latter software, has a particularly strong predictive capability. The high accuracy and sensitivity of the ML results indicate that such a strategy could be very useful for quality control of fiber dispersions. Computer science ; Artificial neural network ; Artificial intelligence ; Support vector machine ; Preprocessor ; Software ; Machine learning ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Mineral Processing and Grinding
Authors: Ioanna Kostopoulou | Andromachi Tzani | Konstantina Chronaki | Kyriakos C. Prousis | Eleni Pontiki | Dimitra Hadjipavlou‐Litina | Anastasia Detsi
Source: Molecules (article, 2023-12-28)
Field: Chemistry — Organic Chemistry

Novel Multi-Target Agents Based on the Privileged Structure of 4-Hydroxy-2-quinolinone In this work, the privileged scaffold of 4-hydroxy-2quinolinone is investigated through the synthesis of carboxamides and hybrid derivatives, as well as through their bioactivity evaluation, focusing on the ability of the molecules to inhibit the soybean LOX, as an indication of their anti-inflammatory activity. Twenty-one quinolinone carboxamides, seven novel hybrid compounds consisting of the quinolinone moiety and selected cinnamic or benzoic acid derivatives, as well as three reverse amides are synthesized and classified as multi-target agents according to their LOX inhibitory and antioxidant activity. Among all the synthesized analogues, quinolinone-carboxamide compounds <b>3h</b> and <b>3s</b>, which are introduced for the first time in the literature, exhibited the best LOX inhibitory activity (IC<sub>50</sub> = 10 μM). Furthermore, carboxamide <b>3g</b> and quinolinone hybrid with acetylated ferulic acid <b>11e</b> emerged as multi-target agents, revealing combined antioxidant and LOX inhibitory activity (<b>3g</b>: IC<sub>50</sub> = 27.5 μM for LOX inhibition, 100% inhibition of lipid peroxidation, 67.7% ability to scavenge hydroxyl radicals and 72.4% in the ABTS radical cation decolorization assay; <b>11e</b>: IC<sub>50</sub> = 52 μM for LOX inhibition and 97% inhibition of lipid peroxidation). The in silico docking results revealed that the synthetic carboxamide analogues <b>3h</b> and <b>3s</b> and NDGA (the reference compound) bind at the same alternative binding site in a similar binding mode. Chemistry ; Moiety ; Carboxamide ; IC50 ; Lipid peroxidation ; Stereochemistry ; Docking (animal) ; Lead compound ; Antioxidant ; Structure–activity relationship ; Cinnamic acid ; Ferulic acid ; Combinatorial chemistry ; Biochemistry ; In vitro Synthesis and biological activity ; Synthesis and Biological Evaluation ; Traditional and Medicinal Uses of Annonaceae
Authors: Zefeng Cai | Ke Wang | Yong Xu | Su‐Huai Wei | Ben Xu
Source: Quantum Frontiers (article, 2023-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A self-adaptive first-principles approach for magnetic excited states Abstract The profound impact of excited magnetic states on the intricate interplay between electron and lattice behaviors in magnetic materials is a topic of great interest. Unfortunately, despite the significant strides that have been made in first-principles methods, accurately tracking these phenomena remains a challenging and elusive task. The crux of the challenge that lies before us is centered on the intricate task of characterizing the magnetic configuration of an excited state, utilizing a first-principle approach that is firmly rooted in the ground state of the system. We propose a versatile self-adaptive spin-constrained density functional theory formalism. By iteratively optimizing the constraining field alongside the electron wave function during energy minimization, we are able to obtain an accurate potential energy surface that captures the longitudinal and transverse variations of magnetization in itinerant ferromagnetic Fe. Moreover, this technique allows us to identify the subtle coupling between magnetic moments and other degrees of freedom by tracking energy variation, providing new insights into the intricate interplay between magnetic interactions, electronic band structure, and phonon dispersion curves in single-layered $\mathrm{CrI} _{3}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>CrI</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:math> . This new methodology represents a significant breakthrough in our ability to probe the complex and multifaceted properties of magnetic systems. Excited state ; Ground state ; Physics ; Wave function ; Computer science ; Condensed matter physics ; Statistical physics ; Atomic physics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Iron-based superconductors research
Authors: Andrew S. Lang
Source: Annals of Chemical Science Research (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Fine-Tuning ChemBERTa-2 for Aqueous Solubility Prediction Annals of Chemical Science Research Fine-Tuning ChemBERTa-2 for Aqueous Solubility Prediction Andrew SID Lang1*, Wei Khiong Chong2 and Jan HR Wörner1 1Department of Computing & Mathematics, Oral Roberts University, USA 1Advent Polytech Co., Ltd., Taipao City, Taiwan *Corresponding author:Andrew SID Lang, Department of Computing & Mathematics, Oral Roberts University, Tulsa, OK, 74171, USA Submission: May 08, 2023;Published: May 19, 2023 DOI: 10.31031/ACSR.2023.04.000578 Volume4 Issue1May , 2023 Annals ; Solubility ; Aqueous solution ; Chemistry ; Environmental science ; History ; Physical chemistry ; Archaeology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Hendrik Verplancke | Martin Diefenbach | Jonas N. Lienert | Mihkel Ugandi | Marios‐Petros Kitsaras | Michael Roemelt | Stella Stopkowicz | Max C. Holthausen
Source: Israel Journal of Chemistry (article, 2023-05-24)
Field: Chemistry — Organic Chemistry

Another Torture Track for Quantum Chemistry: Reinvestigation of the Benzaldehyde Amidation by Nitrogen‐Atom Transfer from Platinum(II) and Palladium(II) Metallonitrenes Abstract We showcase here a dramatic failure of CCSD(T) theory that originates from the pronounced multi‐reference character of a key intermediate formed in the benzaldehyde amidation by N‐atom transfer from Pd(II) and Pt(II) metallonitrenes studied recently in combined experimental and theoretical work. For detailed analysis we devised a minimal model system, for which we established reliable reference energies based on approximate full configuration interaction theory, to assess the performance of single‐reference coupled‐cluster theory up to the CCSDTQ(P) excitation level. While RHF‐based CCSD(T) theory suffered dramatic errors, in one case exceeding 220 kcal mol −1 , we show that the use of broken‐symmetry (BS) or Kohn‐Sham (KS) orbital references yields substantially improved CCSD(T) results. Further, the EOM‐SF‐CCSD(T)(a)* approach met the reference data with excellent accuracy. We applied the KS‐CCSD(T*)‐F12b variant as high‐level part of an ONIOM(KS‐CC:DFT) scheme to reinvestigate the reactivity of the full Pt(II) and Pd(II) metallonitrenes. The revised reaction pathway energetics provide a detailed mechanistic rationale for the experimental observations. Chemistry ; Benzaldehyde ; Computational chemistry ; Reactivity (psychology) ; ONIOM ; Coupled cluster ; Palladium ; Quantum chemistry ; Nitrogen atom ; Molecule ; Reaction mechanism ; Group (periodic table) ; Catalysis ; Organic chemistry Synthesis and Catalytic Reactions ; CO2 Reduction Techniques and Catalysts ; Chemical Reactions and Mechanisms
Authors: P. Gaßmann | Carolin Bohlmann | Valentina Pintus
Source: Polymers (article, 2023-11-28)
Field: Materials Science — Polymers and Plastics

Towards the Understanding of the Aging Behavior of p-PVC in Close Contact with Minced Meat in the Artwork POEMETRIE by Dieter Roth This paper presents scientific investigations into the materiality and aging behavior of a copy of Dieter Roth's multiple <i>PO<sup>E</sup>METRIE</i> (1968), mainly made of p-PVC components and minced meat, with the aim of informing conservation-restoration strategies. The main issues were represented by plasticizer migration, fat diffusion, and leakage, which led to the formation of a sticky surface layer. Replicas of p-PVC without minced meat were prepared and artificially thermally aged, while several techniques were used to investigate both the artwork and the replicas in terms of materials and degradation state. These include UV/Vis imaging, pH measurements, FTIR-ATR, and Py-GC/MS. In addition to showing that p-PVC-based materials composed of slightly different plasticizers were affected by similar degradation pathways (i.e., plasticizer migration, yellowing, etc.), this study reports that fat components were also shown to be unstable, resulting in migration/leakage in different directions, where their degradation amplified that of the p-PVC bags. This work represents a first study of plasticizer migration and fat diffusion in the art and conservation context. Also, an ammine-wax type of lubricant was identified in the most recent p-PVC formulations as the replicas selected for this study, thus providing an important source of information in different polymer-based research areas. Food science ; Contact angle ; Materials science ; Polymer science ; Composite material ; Chemistry Polymer Science and PVC ; Recycling and Waste Management Techniques ; Additive Manufacturing and 3D Printing Technologies
Authors: Fahimeh Hadavimoghaddam | Alexei Rozhenko | Mohammad-Reza Mohammadi | M. Gortani | Peyman Pourafshary | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2023-12-19)
Field: Chemistry — Analytical Chemistry

Modeling crude oil pyrolysis process using advanced white-box and black-box machine learning techniques Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting-CatBoost, Gaussian Process Regression-GPR, Extreme Gradient Boosting-XGBoost), and a white-box approach (Genetic Programming-GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R<sup>2</sup>) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices. Black box ; White box ; Process (computing) ; Computer science ; Box model ; Crude oil ; Artificial intelligence ; Process engineering ; Petroleum engineering ; Machine learning ; Engineering ; Geology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Global Energy and Sustainability Research