jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Irina A. Rodionova | Ali Hosseinnia | Sunyoung Kim | Norman Goodacre | Li Zhang | Zhongge Zhang | Bernhard Ø. Palsson | Peter Uetz | Mohan Babu | Milton H. Saier
Source: Scientific Reports (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

E. coli allantoinase is activated by the downstream metabolic enzyme, glycerate kinase, and stabilizes the putative allantoin transporter by direct binding Allantoin is a good source of ammonium for many organisms, and in Escherichia coli it is utilized under anaerobic conditions. We provide evidence that allantoinase (AllB) is allosterically activated by direct binding of the allantoin catabolic enzyme, glycerate 2-kinase (GlxK) in the presence of glyoxylate. Glyoxylate is known to be an effector of the AllR repressor which regulates the allantoin utilization operons in E. coli. AllB has low affinity for allantoin, but its activation by GlxK leads to increased affinity for its substrate. We also show that the predicted allantoin transporter YbbW (re-named AllW) has allantoin specificity and the protein-protein interaction with AllB. Our results show that the AllB-dependent allantoin degradative pathway is subject to previously unrecognized regulatory mechanisms involving direct protein-protein interactions. Allantoin ; Transporter ; Enzyme ; Biochemistry ; Chemistry ; Kinase ; Cell biology ; Biology ; Gene Enzyme Structure and Function ; Neurological diseases and metabolism ; Prion Diseases and Protein Misfolding
Authors: Guanyi Hou | Runhan Ren | Shang Wei | Yunxuan Weng | Jun Liu
Source: Polymers (article, 2023-07-31)
Field: Materials Science — Polymers and Plastics

Molecular Dynamics Simulation of Polymer Nanocomposites with Supramolecular Network Constructed via Functionalized Polymer End-Grafted Nanoparticles Since the proposal of self-healing materials, numerous researchers have focused on exploring their potential applications in flexible sensors, bionic robots, satellites, etc. However, there have been few studies on the relationship between the morphology of the dynamic crosslink network and the comprehensive properties of self-healing polymer nanocomposites (PNCs). In this study, we designed a series of modified nanoparticles with different sphericity (η) to establish a supramolecular network, which provide the self-healing ability to PNCs. We analyzed the relationship between the morphology of the supramolecular network and the mechanical performance and self-healing behavior. We observed that as η increased, the distribution of the supramolecular network became more uniform in most cases. Examination of the segment dynamics of polymer chains showed that the completeness of the supramolecular network significantly hindered the mobility of polymer matrix chains. The mechanical performance and self-healing behavior of the PNCs showed that the supramolecular network mainly contributed to the mechanical performance, while the self-healing efficiency was dominated by the variation of η. We observed that appropriate grafting density is the proper way to effectively enhance the mechanical and self-healing performance of PNCs. This study provides a unique guideline for designing and fabricating self-healing PNCs with modified Nanoparticles (NPs). Materials science ; Supramolecular chemistry ; Nanocomposite ; Supramolecular polymers ; Nanotechnology ; Polymer ; Polymer network ; Nanoparticle ; Polymer nanocomposite ; Composite material ; Chemistry ; Molecule Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications
Authors: Ashoka Kumar Ratha | Nalini Kanta Barpanda | Prabira Kumar Sethy | Santi Kumari Behera
Source: Ingénierie des systèmes d information (article, 2023-02-28)
Field: Chemistry — Analytical Chemistry

Papaya Fruit Maturity Estimation Using Wavelet and ConvNET The papaya (Carica papaya L.) is a tropical fruit with high commercial value due to its superior nutritional and therapeutic properties.Papayas must be packaged in the fruit industry according to their degree of ripeness.Physically grading papaya fruit using human vision is time-consuming and destructive.A brand-new, non-destructive classification system for papaya fruit development stages is being offered as a result of this study.The project proposes to investigate three classification models: one deep learning method, the DWT approach, and a hybrid approach.A total of 300 papaya fruit sample photos were used in the experiment, 100 of which corresponded to each fruit's three ripeness stages: midripen, ripen, and un-ripen.The maturity level of papaya is estimated using a hybrid network, i.e., the combination of high-level features and an SVM classifier.The high-level features are the integration of deep Features of VGG16 and coefficients of DWT.The accuracy and AUC of the proposed hybrid model are 98% and 100%, respectively. Maturity (psychological) ; Estimation ; Wavelet ; Horticulture ; Biology ; Artificial intelligence ; Mathematics ; Computer science ; Engineering ; Psychology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Khalid M. Alharthy | Marwa Fadhil Alsaffar | Hassan N. Althurwi | Faisal F. Albaqami | Russul Reidh Abass | Aisha Majid Alawi | Sarah Salah Jalal | Shazia Tabassum | Hao Zhang | Peng Wang
Source: Inorganic Chemistry Communications (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Boron nitride nanocage as drug delivery systems for chloroquine, as an effective drug for treatment of coronavirus disease: A DFT study Nanocages ; Drug delivery ; Chloroquine ; Drug ; Boron nitride ; Pharmacology ; Density functional theory ; Materials science ; Nanotechnology ; Medicine ; Chemistry ; Computational chemistry ; Immunology Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Energetic Materials and Combustion
Authors: Jianzhi Xu | Joost M. Bakker | Olga V. Lushchikova | Peter Lievens | Ewald Janssens | Gao‐Lei Hou
Source: Journal of the American Chemical Society (article, 2023-09-27)
Field: Chemistry — Organic Chemistry

Pentagon, Hexagon, or Bridge? Identifying the Location of a Single Vanadium Cation on Buckminsterfullerene Surface Buckminsterfullerene C<sub>60</sub> has received extensive research interest since its discovery. In addition to its interesting intrinsic properties of exceptional stability and electron-accepting ability, the broad chemical tunability by decoration or substitution on the C<sub>60</sub>-fullerene surface makes it a fascinating molecule. However, to date, there is uncertainty about the binding location of such decorations on the C<sub>60</sub> surface, even for a single adsorbed metal atom. In this work, we report the gas-phase synthesis of the C<sub>60</sub>V<sup>+</sup> complex and its in situ characterization by mass spectrometry and infrared spectroscopy with the help of quantum chemical calculations and molecular dynamics simulations. We identify the most probable binding position of a vanadium cation on C<sub>60</sub> above a pentagon center in an η<sup>5</sup>-fashion, demonstrate a high thermal stability for this complex, and explore the bonding nature between C<sub>60</sub> and the vanadium cation, revealing that large orbital and electrostatic interactions lie at the origin of the stability of the η<sup>5</sup>-C<sub>60</sub>V<sup>+</sup> complex. Buckminsterfullerene ; Chemistry ; Vanadium ; Fullerene ; Chemical physics ; Molecule ; Thermal stability ; Atom (system on chip) ; Mass spectrometry ; Infrared spectroscopy ; Computational chemistry ; Pentagon ; Crystallography ; Inorganic chemistry ; Organic chemistry Fullerene Chemistry and Applications ; Advanced Chemical Physics Studies ; Graphene research and applications
Authors: Hisham Alghamdi | Chika Maduabuchi | Abdullah Albaker | Abdulaziz Almalaq | Turki Alsuwian | Ibrahim Alatawi
Source: International Journal of Energy Research (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mn>3.34</a:mn> <a:mo>×</a:mo> <a:msup> <a:mrow> <a:mn>10</a:mn> </a:mrow> <a:mrow> <a:mo>−</a:mo> <a:mn>8</a:mn> </a:mrow> </a:msup> </a:math> and high training and testing regression of 100% and 99.98%, respectively. Photovoltaic system ; Computer science ; Artificial neural network ; Solar cell ; Performance prediction ; Thermoelectric effect ; Renewable energy ; Electronic engineering ; Simulation ; Engineering ; Artificial intelligence ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Solar Thermal and Photovoltaic Systems ; Solar Radiation and Photovoltaics
Authors: Joana Ferreira | Filipa Castro
Source: CrystEngComm (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Advances in protein solubility and thermodynamics: quantification, instrumentation, and perspectives Critical review of the advanced experimental approaches to measure the main thermodynamic parameters and to guide establishing the necessary conditions to perform a protein crystallization assay: methodologies, set-ups, and future directions. Solubility ; Thermodynamics ; Crystallization ; Instrumentation (computer programming) ; Measure (data warehouse) ; Chemistry ; Biochemical engineering ; Materials science ; Computer science ; Physics ; Engineering ; Data mining ; Physical chemistry Crystallization and Solubility Studies ; Protein purification and stability ; Enzyme Structure and Function
Authors: Xuejian Zhou | Wenzheng Liu | Kai Li | Dongqing Lu | Yuan Su | Yanlun Ju | Yulin Fang | Jihong Yang
Source: Foods (article, 2023-12-04)
Field: Chemistry — Analytical Chemistry

Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible–Near-Infrared Spectroscopy Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry. Artificial intelligence ; Ripeness ; Pattern recognition (psychology) ; Wine ; Linear discriminant analysis ; Mathematics ; Support vector machine ; Near-infrared spectroscopy ; Feature (linguistics) ; Computer science ; Chemistry ; Optics ; Physics ; Food science Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Horticultural and Viticultural Research
Authors: Hao Tang | B.Q. Li | Yixuan Song | M. Liu | Haowei Xu | Guoqing Wang | Heejung Chung | Ju Li
Source: Advanced Science (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Reinforcement Learning‐Guided Long‐Timescale Simulation of Hydrogen Transport in Metals Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long-timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter-intuitive hydrogen-vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm, using hydrogen migration to copper (111) surface as an example. Reinforcement learning ; Thermal diffusivity ; Testbed ; Computer science ; Hydrogen ; Diffusion ; Materials science ; Statistical physics ; Simulation ; Thermodynamics ; Chemistry ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Nuclear Materials and Properties
Authors: Aris E. Giannakas | Vassilios K. Karabagias | Dimitrios Moschovas | Areti Leontiou | Ioannis K. Karabagias | Stavros Georgopoulos | Andreas Karydis‐Messinis | Konstantinos Zaharioudakis | Νικόλαος Δ. Ανδρίτσος | George Kehayias | Apostolos Avgeropoulos | Charalampos Proestos | Constantinos E. Salmas
Source: Preprints.org (preprint, 2023-06-07)
Field: Materials Science — Biomaterials

Thymol@activated Carbon Nanohybrid for Low-Density Polyeth-Ylene Based Active Packaging Films for Pork Fillets Shelf-Life Extension Nowadays, bioeconomy and nanotechnology trends push food packaging sector in the re-placement of food additives by biobased antioxidant/antibacterial compounds and their inclusion in nanocarriers to control their release. Herein by following this trend, a rich in thymol-(TO) activated carbon (AC) nanostructure (TO@AC) was prepared and physiochemically characterized with various technics. This TO@AC nanostructure as well as pure AC were extruded with low density polyethylene (LDPE) to develop novel active packaging LDPE/TO@AC and LDPE/AC films. X-ray diffractometry, Fourier Transform Infrared Spectroscopy and Scanning Electron Microscopy measuraments shown high dispersity and capability of both AC and TO@AC in LDPE matrix which was resulted in enhanced tensile and water/oxygen barrier properties of obtained LDPE/AC and LDPE/TO@AC films. LDPE/TO@AC films shown higher elongation at break values and water/oxygen barrier than LDPE/AC films and significant antioxidant activity. TO release kinetics studies shown that by increasing TO@AC content the total TO amount released increased and the constant release rate decreased. Pork fillets wrapped with the optimum active film containing 15 wt.% TO@AC and succeed to prevent lipid oxidation and heme iron loss during storage. Estimation of microbial population of pork fillets shown that this active film could ex-tend the microbiological shelf-life of pork fillets by 2 days Low-density polyethylene ; Active packaging ; Shelf life ; Polyethylene ; Chemical engineering ; Materials science ; Chemistry ; Food packaging ; Food science ; Composite material Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Authors: Nurul Azreen Suhaidi | Mohd Izuan Effendi Halmi | Asraf Alif Rashidi | Mohd Firdaus Mohd Anuar | Khairil Mahmud | Norzila Kusnin | Siti Salwa Abd Gani | Mohd Yunus Shukor
Source: 3 Biotech (article, 2023-04-05)
Field: Materials Science — Materials Chemistry

Colorimetric detection of mercury (Hg2+) using UV–vis spectroscopy and digital image analysis based on gold nanoparticles functionalized with bromelain enzyme Colloidal gold ; Mercury (programming language) ; Biomonitoring ; Detection limit ; Bromelain ; Chromatography ; Biosensor ; Chemistry ; Materials science ; Nanoparticle ; Analytical Chemistry (journal) ; Nanotechnology ; Environmental chemistry ; Computer science ; Enzyme ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Natalia Piqueras-García | María Vergara‐Barberán | Marı́a Jesús Lerma-García | José Manuel Herrero‐Martínez
Source: Advances in Sample Preparation (article, 2023-07-04)
Field: Chemistry — Analytical Chemistry

Aptamer-functionalized magnetic supports for sample preparation In the last few years, aptamer-functionalized materials have been used as promising affinity sorbents for sample preparation purposes. These selective materials have attracted much attention due to their excellent molecular recognition properties, high stability, and the possibility of incorporation onto the surface of different supporting materials, such as nanoparticles, polymers, etc. On the other hand, current trends in extraction techniques encompass the use of miniaturized devices integrated with advanced affinity-based sorbents. Within these sample preparation devices, magnetic nanoparticles have become excellent candidates for the preparation of new functional nanomaterials for the solid-phase extraction processes. In addition to this approach, magnetic elements can be integrated in extraction devices (e.g., stir bars, stir cakes, etc.) to provide efficient extraction/stirring integrated techniques able to simplify the extraction and improve the performance. This review gives an overview of the literature published regarding aptamer-based sorbents using as host supports magnetic nanoparticles and integrated stirred units. For this purpose, the most relevant developments and applications achieved in the last seven years (from 2017 to 2023) in sample preparation by the integration of these affinity-based materials in extraction techniques such as magnetic solid-phase extraction and stir bar sorptive extraction will be reviewed. Aptamer ; Magnetic nanoparticles ; Extraction (chemistry) ; Sample preparation ; Materials science ; Nanotechnology ; Solid phase extraction ; Nanomaterials ; Polymer ; Nanoparticle ; Process engineering ; Chromatography ; Chemistry ; Engineering ; Composite material Analytical chemistry methods development ; Advanced biosensing and bioanalysis techniques ; Molecular Sensors and Ion Detection
Authors: P. C. Agu | Kooffreh Okon
Source: Zastita materijala (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

The inhibition of mild steel corrosion by papaya and neem extracts This study examined earlier research on using papaya and neem extracts as inhibitors to minimize the corrosion of mild steel in a variety of corrosive situations. The potential inhibitory characteristics of plant extracts to potentially replace the hitherto used, well-known inhibitors that are harmful to the people handling them as well as the environment, inspired several scholars to conduct corrosion inhibition tests on metals using plant extracts. The findings of earlier research demonstrated that the maximal inhibitory efficiency provided by neem leaf extract to prevent the degradation of carbon steel in a hydrochloric acid (1 M) medium was 97%, while 86% was observed for the protection in H2SO4 (1 M) solution. The extract from Carica papaya leaves was shown to have up to 83% maximum inhibitory efficacy for preventing mild steel corrosion in HCl (1 M). It has been revealed that when extract concentration increased, the rate of steel corrosion reduced. Additionally, papaya leaves' corrosion-inhibiting mechanism was said to occur mostly in the cathodic area. Both plant-leaf extracts (Papaya and Neem) have been reported to have adsorption qualities that, for the most part, agreed with the Langmuir adsorption isotherm model. Carica ; Corrosion ; Hydrochloric acid ; Adsorption ; Langmuir adsorption model ; Carbon steel ; Chemistry ; Cathodic protection ; Neem oil ; Nuclear chemistry ; Metallurgy ; Horticulture ; Materials science ; Biology ; Organic chemistry ; Electrochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Electrodeposition and Electroless Coatings