jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sekar Manikandan | Adhinarayanapuram Jayaram Devanathan Nanthakumar
Source: Periodica Polytechnica Chemical Engineering (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Experimental Investigation and Specific Heat Capacity Prediction of Graphene Nanoplatelet-Infused SAE10W Oil Using Artificial Neural Networks This research aims to develop an artificial neural network (ANN) model to predict the specific heat capacity (SHC) of graphene/SAE10W oil nanofluid. An experimental investigation of the SHC of graphene nanoplatelets infused in SAE10W oil was carried out using a thermal constant analyzer. In experimental testing the volume percentage and fluid temperature range vary from 0.05 to 0.15 and 293 K to 353 K, respectively. The experimental data shows the graphene/SAE10W oil nanofluid exhibited a reduction in SHC relative to the base fluid. The ANN model was developed using experimental data to predict the specific heat capacity of graphene/SAE10W oil nanofluid. During ANN model training, the correlation coefficient and mean square error of 0.999 and 6.592 × 10 –6 were achieved, respectively. Compared to experimental values, the ANN model predicts SHC with a 0.45 error percentage. Additionally, a mathematical model has been developed to predict the SHC of graphene/SAE10W oil nanofluids using curve fitting. The data obtained from the developed mathematical model showed excellent correlation with all experimental values, with an error percentage of ±0.42. Hence, it is concluded that both models provide an optimal approach for estimating their SHC. Artificial neural network ; Materials science ; Graphene ; Heat capacity ; Chemical engineering ; Biological system ; Nanotechnology ; Computer science ; Artificial intelligence ; Thermodynamics ; Engineering ; Physics High voltage insulation and dielectric phenomena
Authors: Samaneh Sanei Movafagh | Sadegh Salehzadeh
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Can we quantitatively evaluate the mutual impacts of intramolecular metal–ligand bonds the same as intermolecular noncovalent bonds? In this paper, we have reviewed several equations for calculating the cooperative energy of two chemical bonds between three fragments/species, regardless of whether they are atoms, ions or molecules, and whether the bonds between them are intra- or intermolecular. It is emphasized that two chemical bonds upon cooperation in a new compound change the bond dissociation energy of each other exactly by the same quantitative value, their cooperative energy, regardless of the nature of the bonds or whether one bond is very weak and another one is very strong. However, the final benefit/drawback of weak bonds from this cooperation can be considerably larger than that of strong bonds. The above statements are supported by a computational study on the various types of inter- and intramolecular chemical bonds. Intramolecular force ; Intermolecular force ; Non-covalent interactions ; Chemistry ; Ligand (biochemistry) ; Bond ; Metal ; Computational chemistry ; Hydrogen bond ; Chemical physics ; Molecule ; Stereochemistry ; Organic chemistry ; Receptor Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Organometallic Complex Synthesis and Catalysis
Authors: Kara A. Zielinski | Cole Dolamore | Kevin M. Dalton | Nathan Smith | John Termini | Robert Henning | V. Šrajer | Doeke R. Hekstra | Lois Pollack | Mark A. Wilson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-20)
Field: Materials Science — Materials Chemistry

Resolving DJ-1 Glyoxalase Catalysis Using Mix-and-Inject Serial Crystallography at a Synchrotron DJ-1 (PARK7) is an intensively studied protein whose cytoprotective activities are dysregulated in multiple diseases. DJ-1 has been reported as having two distinct enzymatic activities in defense against reactive carbonyl species that are difficult to distinguish in conventional biochemical experiments. Here, we establish the mechanism of DJ-1 using a synchrotron-compatible version of mix-and-inject-serial crystallography (MISC), which was previously performed only at XFELs, to directly observe DJ-1 catalysis. We designed and used new diffusive mixers to collect time-resolved Laue diffraction data of DJ-1 catalysis at a pink beam synchrotron beamline. Analysis of structurally similar methylglyoxal-derived intermediates formed through the DJ-1 catalytic cycle shows that the enzyme catalyzes nearly two turnovers in the crystal and defines key aspects of its glyoxalase mechanism. In addition, DJ-1 shows allosteric communication between a distal site at the dimer interface and the active site that changes during catalysis. Our results rule out the widely cited deglycase mechanism for DJ-1 action and provide an explanation for how DJ-1 produces L-lactate with high chiral purity. Synchrotron ; Lactoylglutathione lyase ; Catalysis ; Dimer ; Chemistry ; Methylglyoxal ; Allosteric regulation ; Catalytic cycle ; Crystallography ; Stereochemistry ; Enzyme ; Biochemistry ; Physics ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Biochemical and Molecular Research
Authors: Noor Fadhilah Rahmat | Mohd Shaiful Sajab | A. Atiqah | Gongtao Ding | Chin Hua Chia
Source: BioResources (article, 2024-01-16)
Field: Materials Science — Biomaterials

Thermoplastic sugar palm starch reinforced graphene nanoplatelets for sustainable biocomposite films Graphene nanoplatelets (GNP) were incorporated into thermoplastic starch (TPS) films, and effects on water absorption and mechanical properties were investigated. GNP inclusion formed a barrier that significantly reduced water absorption, resulting in denser TPS/GNP films. Fourier-transform infrared spectroscopy (FTIR) revealed changes in chemical interactions, and FESEM analysis showed improved GNP dispersion at this concentration. Water contact angle results indicated increased hydrophobicity with higher GNP content. Positive influences on mechanical properties, such as tensile strength and Young’s modulus, were observed at 12 wt% GNP, but excessive GNP content caused agglomeration and reduced ductility. The study results highlight the potential of GNP-reinforced TPS films for improved water resistance and mechanical properties, emphasizing the need for careful optimization in future research. Biocomposite ; Materials science ; Thermoplastic ; Starch ; Palm ; Composite material ; Sugar ; Graphene ; Food science ; Composite number ; Nanotechnology ; Biology biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Natural Fiber Reinforced Composites
Authors: Annika Brünje | Magdalena Füßl | Jürgen Eirich | J. Boyer | Paulina Heinkow | Ulla Neumann | Minna M. Konert | Aiste Ivanauskaite | Julian Seidel | Shin‐Ichiro Ozawa | Wataru Sakamoto | Thierry Meinnel | Dirk Schwarzer | Paula Mulo | Carmela Giglione | Iris Finkemeier
Source: Molecular & Cellular Proteomics (article, 2024-09-28)
Field: Materials Science — Materials Chemistry

The Plastidial Protein Acetyltransferase GNAT1 Forms a Complex With GNAT2, yet Their Interaction Is Dispensable for State Transitions In BriefProtein acetylation is a common cotranslational and posttranslational modification in chloroplasts.Here, we characterize GNAT1, an acetyltransferase that adds acetyl groups to N-termini and lysine side chains.GNAT1 is closely related to GNAT2, which regulates state transitions of the photosynthetic antenna proteins.Our findings show shared Nterminal substrate sites between GNAT1 and GNAT2 in vivo.Both enzymes form a complex, but this interaction is not essential for state transitions. Highlights GNAT1 is a plastidial acetyltransferase with dual activity in Arabidopsis. The N-terminal substrates of GNAT1 form a subset of the substrate set of GNAT2. Predicted structure models of GNAT1 and GNAT2 show a high architectural similarity. Absence of GNAT1 neither impacts state transitions nor acclimation to high light. Stable interactions were observed between GNAT1 & GNAT2 and GNAT2 & GNAT3. Acetyltransferase ; Acetylation ; Acetyltransferases ; Mutant ; Immunoprecipitation ; Biochemistry ; Chloroplast ; Biology ; Thylakoid ; Histone acetyltransferase ; Chemistry ; Wild type ; Gene Enzyme Structure and Function ; Photosynthetic Processes and Mechanisms ; Mass Spectrometry Techniques and Applications
Authors: Tania Tabussam | Hina Shehnaz | Muhammad Irfan Majeed | Haq Nawaz | Abeer Ahmed Alghamdi | Muhammad Adnan Iqbal | Muhammad Shahid | Urwa Shahid | Rabiea Umer | Muhammad Tjammal Rehman | Umer Farooq | Ahmad Hassan | Muhammad Imran
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman spectroscopy for studying the interaction of organometallic compound bis(1,3-dihexylimidazole-2-yl) silver( <scp>i</scp> ) hexafluorophosphate ( <scp>v</scp> ) with the biofilm of <i>Escherichia coli</i> <i>Escherichia coli</i> biofilms are a major cause of gastrointestinal tract diseases, such as esophageal, stomach and intestinal diseases. Nowadays, these are the most commonly occurring diseases caused by consuming contaminated food. In this study, we evaluated the efficacy of probiotics in controlling multidrug-resistant <i>E. coli</i> and reducing its ability to form biofilms. Our results substantiate the effective use of probiotics as antimicrobial alternatives and to eradicate biofilms formed by multidrug-resistant <i>E. coli</i>. In this research, surface enhanced Raman spectroscopy (SERS) was utilized to identify and evaluate <i>Escherichia coli</i> biofilms and their response to the varying concentrations of the organometallic compound bis(1,3-dihexylimidazole-2-yl) silver(i) hexafluorophosphate (v). Given the escalating challenge of antibiotic resistance in bacteria that form biofilms, understanding the impact of potential antibiotic agents is crucial for the healthcare sector. The combination of SERS with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) enabled the detection and characterization of the biofilm, providing insights into the biochemical changes induced by the antibiotic candidate. The identified SERS spectral features served as indicators for elucidating the mode of action of the potential drug on the biofilm. Through PCA and PLS-DA, metabolic variations allowing the differentiation and classification of unexposed biofilms and biofilms exposed to different concentrations of the synthesized antibiotic were successfully identified, with 95% specificity, 96% sensitivity, and a 0.75 area under the curve (AUC). This research underscores the efficiency of surface enhanced Raman spectroscopy in differentiating the impact of potential antibiotic agents on <i>E. coli</i> biofilms. Hexafluorophosphate ; Escherichia coli ; Biofilm ; Chemistry ; Raman spectroscopy ; Organometallic chemistry ; Nuclear chemistry ; Combinatorial chemistry ; Bacteria ; Organic chemistry ; Ionic liquid ; Biochemistry ; Catalysis ; Biology ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Bacterial biofilms and quorum sensing ; Nanoparticles: synthesis and applications
Authors: Abdil Karakan
Source: Processes (article, 2024-09-21)
Field: Chemistry — Analytical Chemistry

Potato Beetle Detection with Real-Time and Deep Learning In this study, deep learning methods were used to detect potato beetles (Leptinotarsa decemlineata) on potato plants. High-resolution images were taken of fields with the help of a drone. Since these images were large in size, each one was divided into six equal parts. Then, according to the image, the potato beetles were divided into three classes: adult, late-stage potato beetle, and no beetles. A data set was created with 3000 images in each class, making 9000 in total. Different filters were applied to the images that made up the data set. In this way, problems that may have arisen from the camera in real-time detection were minimized. At the same time, the accuracy rate was increased. The created data set was used with six different deep learning models: MobileNet, InceptionV3, ResNet101, AlexNet, DenseNet121, and Xception. The deep learning models were tested with Sgd, Adam, and Rmsprop optimization methods and their performances were compared. In order to evaluate the success of the models more accurately, they were tested on a second data set created with images taken from a different field. As a result of this study, the highest accuracy of 99.81% was obtained. In the test results from a second field that did not exist in the data set, 92.95% accuracy was obtained. The average accuracy rate was 96.30%. Colorado potato beetle ; Artificial intelligence ; Computer science ; Environmental science ; Real-time computing ; Horticulture ; Biology ; PEST analysis Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Qinghu Guo | Pengbin Wang | Xiangyu Meng | Dongxu Li | Zhonghua Yao | Huajun Sun | Hua Hao | Hanxing Liu | Shujun Zhang
Source: Journal of the American Ceramic Society (article, 2024-01-03)
Field: Materials Science — Materials Chemistry

Low‐temperature sintering Sm‐doped PMN–PZ–PT multilayer ceramic actuator with high strain and temperature stability Abstract Multilayer ceramic actuators find extensive utility in applications requiring precise displacement control or force output, due to their advantages of high precision control, rapid response, low driving voltage, low power consumption, and so on. For practical applications, there is a need for high‐performance multilayer ceramic actuators (MLAs) that combine cost‐effectiveness, efficiency, and temperature stability. In this work, 1.5 mol%Sm‐doped 0.25 Pb(Mg 1/3 Nb 2/3 )O 3 –0.36PbZrO 3 –0.39PbTiO 3 piezoelectric ceramics with PbO–CuO as sintering aid were fabricated. The addition of PbO–CuO significantly reduced the sintering temperature by 300°C, while having minimal impact on the piezoelectric properties. The optimized sintering condition, achieved at a low sintering temperature of 950°C with the introduction of a tiny 0.4 wt% sintering aid, yields impressive piezoelectric strain coefficient d 33 * = 690 pm/V. This substantiates the viability of employing low‐cost Ag/Pd (90/10) as inner electrode, which can be co‐fired with ceramic layer to fabricate MLA. This innovation enables a remarkable displacement of 1 µm at a low driving voltage of 200 V across a 9‐layers piezoelectric actuator, accompanied by high‐temperature stability, exhibiting &lt;20% variation over the temperature range of 30–250°C. These results underscore a great potential for high‐performance piezoelectric actuators over a broad temperature range. Sintering ; Materials science ; Ceramic ; Piezoelectricity ; Atmospheric temperature range ; Actuator ; Composite material ; Doping ; Temperature coefficient ; Optoelectronics ; Electrical engineering ; Thermodynamics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Acoustic Wave Resonator Technologies
Authors: Fuhao Zhang | Lukasz Kurgan
Source: Computational and Structural Biotechnology Journal (review, 2024-12-17)
Field: Materials Science — Materials Chemistry

Evaluation of predictions of disordered binding regions in the CAID2 experiment A large portion of the Intrinsically Disordered Regions (IDRs) in protein sequences interact with proteins, nucleic acids, and other types of ligands. Correspondingly, dozens of sequence-based predictors of binding IDRs were developed. A recently completed second community-based Critical Assessments of protein Intrinsic Disorder prediction (CAID2) evaluated 32 predictors of binding IDRs. However, CAID2 considered a rather narrow scenario by testing on 78 proteins with binding IDRs and not differentiating between different ligands, in spite that virtually all predictors target IDRs that interact with specific types of ligands. In that scenario, several intrinsic disorder predictors predict binding IDRs with accuracy equivalent to the best predictors of binding IDRs since large majority of IDRs in the 78 test proteins are binding. We substantially extended the CAID2's evaluation by using the entire CAID2 dataset of 348 proteins and considering several arguably more practical scenarios. We assessed whether predictors accurately differentiate binding IDRs from other types of IDRs and how they perform when predicting IDRs that interact with different ligand types. We found that intrinsic disorder predictors cannot accurately identify binding IDRs among other disordered regions, majority of the predictors of binding IDRs are ligand type agnostic (i.e., they cross predict binding in IDRs that interact with ligands that they do not cover), and only a handful of predictors of binding IDRs perform relatively well and generate reasonably low amounts of cross predictions. We also suggest a number of future research directions that would move this active field of research forward. Computational biology ; Environmental science ; Computer science ; Statistical physics ; Biology ; Physics Enzyme Structure and Function ; Superconducting Materials and Applications ; Protein Structure and Dynamics
Authors: Qinglong Meng | Shunan Feng | Tao Tan | Qingchun Wen | Jing Shang
Source: Food Science & Nutrition (article, 2024-04-14)
Field: Chemistry — Analytical Chemistry

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with <i>R</i> <sup>2</sup> <sub>P</sub> and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats. Partial least squares regression ; Support vector machine ; Weighting ; Extreme learning machine ; Artificial intelligence ; Computer science ; Least squares support vector machine ; Linear discriminant analysis ; Calibration ; Sampling (signal processing) ; Artificial neural network ; Pattern recognition (psychology) ; Mathematics ; Statistics ; Biological system ; Machine learning ; Telecommunications ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Kena Zhang | Qingmeng Liu | Yukun Wang | Xigui Liu | Xiaoxia Zhou | Bing Yan
Source: Nanomaterials (review, 2024-12-03)
Field: Materials Science — Materials Chemistry

Advances and Challenges in Tracking Interactions Between Plants and Metal-Based Nanoparticles Metal-based nanoparticles (MNPs) are increasingly prevalent in the environment due to both natural processes and human activities, leading to direct interactions with plants through soil, water, and air exposure that can have beneficial and detrimental effects on plant growth and health. Understanding the uptake, translocation, and transformation of MNPs in plants is crucial for assessing environmental risks and leveraging nanotechnology in agriculture. However, accurate analysis of MNPs in plant tissues poses significant challenges due to complex plant matrices and the dynamic nature of nanoparticles. This short review summarizes recent advances in analytical methods for determining MNP-plant interactions, focusing on pre-processing and quantitative nanoparticle analysis. It highlights the importance of selecting appropriate extraction and analytical techniques to preserve nanoparticle integrity and accurate quantification. Additionally, recent advances in mass spectrometry, microscopy, and other spectroscopic techniques that improve the characterization of MNPs within plant systems are discussed. Future perspectives highlight the need to develop real-time in situ monitoring techniques and sensitive tools for characterizing nanoparticle biotransformation. Nanoparticle ; Tracking (education) ; Nanotechnology ; Materials science ; Environmental science ; Psychology Nanoparticles: synthesis and applications ; Photoacoustic and Ultrasonic Imaging ; Advanced Nanomaterials in Catalysis
Authors: Máté Ficzere | Lilla Alexandra Mészáros | Anna Diószegi | Zoltán Bánrévi | Attila Farkas | Sándor Lenk | Dorián László Galata | Zsombor Kristóf Nagy
Source: International Journal of Pharmaceutics (article, 2024-05-01)
Field: Chemistry — Analytical Chemistry

UV imaging for the rapid at-line content determination of different colourless APIs in their tablets with artificial neural networks This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with different UV induced fluorescent properties according to the measured solid fluorescent spectra. Images were captured with a LED-based UV illumination (385-395 nm) of tablets containing amlodipine or valsartan and common tableting excipients. Blue or green colour components from the RGB colour space were extracted from the images and used as an input dataset to execute API content prediction with artificial neural networks. The traditional destructive, solution-based transmission UV measurement was applied as reference method. After the optimization of the number of hidden layer neurons it was found that the relative error of the content prediction was 4.41 % and 3.98 % in the case of amlodipine and valsartan containing tablets respectively. The results open the possibility to use the proposed UV imaging-based system as a rapid, in-line tool for 100 % API content screening in order to greatly improve pharmaceutical quality control and process understanding. Amlodipine ; Valsartan ; Artificial neural network ; Computer science ; Materials science ; Chromatography ; Biological system ; Chemistry ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Analytical Methods in Pharmaceuticals