jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sara Janowska | Sylwia Andrzejczuk | Piotr Gawryś | Monika Wujec
Source: Molecules (article, 2023-07-21)
Field: Chemistry — Organic Chemistry

Synthesis and Antimicrobial Activity of New Mannich Bases with Piperazine Moiety A series of novel Mannich bases were designed, synthesized, and screened for their antimicrobial activity. The target compounds were synthesized from 4-(3-chlorophenyl)-5-(3-fluorophenyl)-2,4-dihydro-3<i>H</i>-1,2,4-triazole-3-thione and different piperazine derivatives. The structures of the products were confirmed by <sup>1</sup>H and <sup>13</sup>C NMR and elemental analysis. The activity of piperazine derivatives against bacteria (Gram-positive: <i>Staphylococcus epidermidis</i>, <i>Staphylococcus aureus</i>, <i>Micrococcus luteus</i>, <i>Bacillus cereus</i>, and <i>Bacillus subtilis</i>; Gram-negative: <i>Escherichia coli</i>, <i>Pseudomonas aeruginosa</i>, <i>Klebsiella pneumoniae</i>, and <i>Proteus mirabilis</i>) and yeasts (<i>Candida glabrata</i>, <i>Candida krusei</i>, and <i>Candida parapsilosis</i>) was determined by the minimum inhibitory concentration and minimum bactericidal concentration values. Significant activity was observed against Gram-positive bacteria, mainly staphylococci (<b>PG7</b>-<b>PG8</b>) and bacteria of the genes of <i>Micrococcus</i> and <i>Bacillus</i> (<b>PG1-3</b>), as well as selected strains of Gram-negative bacteria, including bacteria of the <i>Enterobacteriaceae</i> family (<b>PG7</b>), while all tested compounds showed high fungistatic activity against <i>Candida</i> spp. yeasts, especially <i>C. parapsilosis</i>, with MICs ranging from 0.49 µg/mL (<b>PG7</b>) to 0.98 µg/mL (<b>PG8</b>) and 62.5 µg/mL (<b>PG1-3</b>). In conclusion, the results obtained confirm the multidirectional antimicrobial activity of the newly synthesized piperazine derivatives. Furthermore, in silico studies suggest that the tested compounds are likely to have good oral bioavailability. The results obtained will provide valuable data for further research into this interesting group of compounds. The library of compounds obtained is still the subject of pharmacological research aimed at finding new interesting biologically active compounds. Candida parapsilosis ; Micrococcus luteus ; Antimicrobial ; Candida krusei ; Bacillus cereus ; Staphylococcus epidermidis ; Candida glabrata ; Chemistry ; Microbiology ; Gram-positive bacteria ; Bacillus subtilis ; Antibacterial activity ; Bacteria ; Proteus vulgaris ; Staphylococcus aureus ; Biology ; Candida albicans ; Candida tropicalis Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Raid Alrowais | Rania Saber Yousef | Osama konsowa Ahmed | Mohamed Mahmoud-Aly | Mahmoud M. Abdel daiem | Noha Said
Source: Environmental Sciences Europe (article, 2023-11-08)
Field: Chemistry — Organic Chemistry

Enhanced detoxification methods for the safe reuse of treated olive mill wastewater in irrigation Abstract Olive Mill Wastewater (OMWW) is produced in large quantities and contains high levels of nutrients that can be reused for irrigation, reducing the demand for freshwater resources. However, OMWW is phytotoxic and expensive to treat, making it important to develop more cost-effective treatment methods. This study aims to investigate an integrated detoxification treatment sequence consisting of acid precipitation, Fenton oxidation, and electrical coagulation to safely reuse OMWW for barley germination. Raw, treated and diluted OMWW (25% and 50% OMWW) were tested. The results showed that raw and diluted OMWW suppressed seed germination at all concentrations, while diluted treated OMWW enhanced seed germination and plant growth. In addition, treated OMWW (acid precipitation treatment) at 25% dilution reported 0% phytotoxicity significantly improved plant growth, where plant fresh weight (FW) reached 123.33 mg. Moreover, α-amylase, lipase, and protease enzyme activity confirmed the superior enhancement of barley growth parameters, where the highest enzyme activity value recoded 0.870 mg maltose/g FW. The integrated treatments reduced detoxification by 97.90% for total phenolic, 98.37% for total flavonoids, and 99.18% for total tannins. Reductions of around 95.78%, 60.00%, and 78.90% in total organic carbon, electric conductivity, and total solids, respectively, were achieved. A significant decrease in heavy metals was observed with removal ratios 98.64%, 94.80%, 96.88%, and 95.72% for Fe, Cu, Mn, and Zn, respectively. Seedling Vigor Index as an indicator of crop productivity was successfully predicted using neural network modeling. Therefore, the applied method can be used as a fertilizer to support plant growth and reduce fertilization costs. Wastewater ; Chemistry ; Germination ; Total suspended solids ; Phytotoxicity ; Sewage treatment ; Agronomy ; Chemical oxygen demand ; Environmental science ; Environmental engineering ; Biology Edible Oils Quality and Analysis ; Selenium in Biological Systems ; Sesame and Sesamin Research
Authors: Ivan Ivanov | V. A. Skryshevsky | Ali Belarouci
Source: ACS Omega (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Engineering Porous Silicon-Based Microcavity for Chemical Sensing In this article, the authors theoretically and experimentally investigated ways to improve the efficiency of porous silicon (PS)-based optical microcavity sensors as a 1D/2D host matrix for electronic tongue/nose systems. The transfer matrix method was used to compute reflectance spectra of structures with different [<i>n</i><sub>L</sub><i>n</i><sub>H</sub>] sets of low <i>n</i><sub>L</sub> and high <i>n</i><sub>H</sub> bilayer refractive indexes, the cavity position λ<sub>c</sub>, and the number of bilayers <i>N</i><sub>bi</sub>. Sensor structures were prepared by electrochemically etching a silicon wafer. The kinetics of adsorption/desorption processes of ethanol-water-based solution was monitored in real time with a reflectivity probe-based setup. It was theoretically and experimentally demonstrated that the sensitivity of the microcavity sensor is higher for structures with refractive indexes in the lower range (and the corresponding porosity values in the upper range). The sensitivity is also improved for structures with the optical cavity mode (λ<sub>c</sub>) adjusted toward longer wavelengths. The sensitivity of a distributed Bragg reflector (DBR) with cavity increases for a structure with cavity position λ<sub>c</sub> in the long wavelength region. The full width at half maximum (fwhm<sub>c</sub>) of the microcavity is smaller and the quality factor of microcavity (<i>Q</i><sub>c</sub>) is higher for the DBR with a larger number of structure layers <i>N</i><sub>bi</sub>. The experimental results are in good agreement with the simulated data. We believe that our results can help in developing rapid, sensitive, and reversible electronic tongue/nose sensing devices based on a PS host matrix. Materials science ; Porous silicon ; Wafer ; Distributed Bragg reflector ; Optoelectronics ; Optical microcavity ; Refractive index ; Silicon ; Etching (microfabrication) ; Transfer-matrix method (optics) ; Bilayer ; Optics ; Wavelength ; Nanotechnology ; Laser ; Layer (electronics) ; Chemistry Silicon Nanostructures and Photoluminescence ; Gas Sensing Nanomaterials and Sensors ; Nanowire Synthesis and Applications
Authors: Vahid Nasir | Syed Danish Ali | Ahmad Mohammadpanah | Sameen Raut | Mohamad Nabavi | Joseph Dahlen | Laurence R. Schimleck
Source: Wood and Fiber Science (article, 2023-07-23)
Field: Chemistry — Organic Chemistry

Fiber Quality Prediction Using Nir Spectral Data: Tree-Based Ensemble Learning VS Deep Neural Networks The growing applications of near infrared (NIR) spectroscopy in wood quality control and monitoring necessitates focusing on data-driven methods to develop predictive models. Despite the advancements in analyzing NIR spectral data, literature on wood science and engineering has mainly uti- lizedthe classic model development methods, such as principal component analysis (PCA) regression or partial least squares (PLS) regression, with relatively limited studies conducted on evaluating machine learning (ML) models, and speciﬁcally, artiﬁcial neural networks (ANNs). This couldpotentially limit the performance of predictive models, speciﬁcally for some wood properties, such as tracheid width that are both time-consuming tomeasure and challenging to predict using spectral data. This study aims to enhance the prediction accuracy for tracheid width using deep neural networks and tree-based ensemble learning algorithms on a dataset consisting of 2018 samples and 692 features (NIR spectra wavelengths). Accord- ingly, NIR spectra were fed into multilayer perceptron (MLP), 1 dimensional-convolutional neural net- works (1D-CNNs), random forest, TreeNet gradient-boosting, extreme gradient-boosting (XGBoost), and light gradient-boosting machine (LGBM). It was of interest to study the performance of the models with and without applying PCA to assess how effective they would perform when analyzing NIR spectra with- out employing dimensionality reduction on data. It was shown that gradient-boosting machines outper- formed the ANNs regardless of the number of features (data dimension). Allthe models performed better without PCA. It is concluded that tree-based gradient-boosting machines could be effectively used for wood characterization utilizing a medium-sized NIR spectral dataset. Random forest ; Artificial neural network ; Gradient boosting ; Artificial intelligence ; Boosting (machine learning) ; Dimensionality reduction ; Principal component analysis ; Multilayer perceptron ; Computer science ; Extreme learning machine ; Pattern recognition (psychology) ; Decision tree ; Convolutional neural network ; Machine learning ; Support vector machine Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Steve P. Meisburger | David A. Case | Nozomi Ando
Source: Nature Communications (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

Robust total X-ray scattering workflow to study correlated motion of proteins in crystals The breathing motions of proteins are thought to play a critical role in function. However, current techniques to study key collective motions are limited to spectroscopy and computation. We present a high-resolution experimental approach based on the total scattering from protein crystals at room temperature (TS/RT-MX) that captures both structure and collective motions. To reveal the scattering signal from protein motions, we present a general workflow that enables robust subtraction of lattice disorder. The workflow introduces two methods: GOODVIBES, a detailed and refinable lattice disorder model based on the rigid-body vibrations of a crystalline elastic network; and DISCOBALL, an independent method of validation that estimates the displacement covariance between proteins in the lattice in real space. Here, we demonstrate the robustness of this workflow and further demonstrate how it can be interfaced with MD simulations towards obtaining high-resolution insight into functionally important protein motions. Computation ; Workflow ; Lattice (music) ; Scattering ; Robustness (evolution) ; Computer science ; Physics ; Statistical physics ; Biological system ; Algorithm ; Computational physics ; Chemistry ; Acoustics ; Biology ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced NMR Techniques and Applications
Authors: Amine Slassi | Linda‐Sheila Medondjio | Andrea Padovani | Francesco Tavanti | Xu He | Sergiu Clima | Daniele Garbin | B. Kaczer | Luca Larcher | Pablo Ordejón | Arrigo Calzolari
Source: Advanced Electronic Materials (article, 2023-02-07)
Field: Materials Science — Materials Chemistry

Device‐to‐Materials Pathway for Electron Traps Detection in Amorphous GeSe‐Based Selectors Abstract The choice of the ideal material employed in selector devices is a tough task both from the theoretical and experimental side, especially due to the lack of a synergistic approach between techniques able to correlate specific material properties with device characteristics. Using a material‐to‐device multiscale technique, a reliable protocol for an efficient characterization of the active traps in amorphous GeSe chalcogenide is proposed. The resulting trap maps trace back the specific features of materials responsible for the measured findings, and connect them to an atomistic description of the sample. The metrological approach can be straightforwardly extended to other materials and devices, which is very beneficial for an efficient material‐device codesign and the optimization of novel technologies. Materials science ; Amorphous solid ; Characterization (materials science) ; Chalcogenide ; Trap (plumbing) ; TRACE (psycholinguistics) ; Protocol (science) ; Nanotechnology ; Electron ; Optoelectronics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Quantum Dots Synthesis And Properties
Authors: Sergey V. Gudkov | Т. А. Матвеева | Ruslan M. Sarimov | Alexander V. Simakin | Е. В. Степанова | Maksim Moskovskiy | Alexey S. Dorokhov | Andrey Yu. Izmailov
Source: AgriEngineering (article, 2023-10-09)
Field: Chemistry — Analytical Chemistry

Optical Methods for the Detection of Plant Pathogens and Diseases (Review) Plant diseases of an infectious nature are the reason for major economic losses in agriculture throughout the world. The early, rapid and non-invasive detection of diseases and pathogens is critical for effective control. Optical diagnostic methods have a high speed of analysis and non-invasiveness. The review provides a general description of such methods and also discusses in more detail methods based on the scattering and absorption of light in the UV, Vis, IR and terahertz ranges, Raman scattering and LiDAR technologies. The application of optical methods to all parts of plants, to a large number of groups of pathogens, under various data collection conditions is considered. The review reveals the diversity and achievements of modern optical methods in detecting infectious plant diseases, their development trends and their future potential. Remote sensing ; Environmental science ; Nanotechnology ; Biotechnology ; Biology ; Materials science ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote Sensing in Agriculture
Authors: Negar Mahmoudi | E.A. Mohamed | Shiva Soltani Dehnavi | Lilith M. Caballero Aguilar | Alan R. Harvey | Clare L. Parish | Richard J. Williams | David R. Nisbet
Source: Advanced Science (review, 2023-11-29)
Field: Materials Science — Biomaterials

Calming the Nerves via the Immune Instructive Physiochemical Properties of Self‐Assembling Peptide Hydrogels Current therapies for the devastating damage caused by traumatic brain injuries (TBI) are limited. This is in part due to poor drug efficacy to modulate neuroinflammation, angiogenesis and/or promoting neuroprotection and is the combined result of challenges in getting drugs across the blood brain barrier, in a targeted approach. The negative impact of the injured extracellular matrix (ECM) has been identified as a factor in restricting post-injury plasticity of residual neurons and is shown to reduce the functional integration of grafted cells. Therefore, new strategies are needed to manipulate the extracellular environment at the subacute phase to enhance brain regeneration. In this review, potential strategies are to be discussed for the treatment of TBI by using self-assembling peptide (SAP) hydrogels, fabricated via the rational design of supramolecular peptide scaffolds, as an artificial ECM which under the appropriate conditions yields a supramolecular hydrogel. Sequence selection of the peptides allows the tuning of these hydrogels' physical and biochemical properties such as charge, hydrophobicity, cell adhesiveness, stiffness, factor presentation, degradation profile and responsiveness to (external) stimuli. This review aims to facilitate the development of more intelligent biomaterials in the future to satisfy the parameters, requirements, and opportunities for the effective treatment of TBI. Self-healing hydrogels ; Neuroprotection ; Traumatic brain injury ; Neuroinflammation ; Extracellular matrix ; Regeneration (biology) ; Self-assembling peptide ; Nanotechnology ; Materials science ; Neuroscience ; Neuroregeneration ; Medicine ; Biomedical engineering ; Cell biology ; Inflammation ; Psychology ; Biology ; Immunology Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Polydiacetylene-based materials and applications
Authors: Víctor Díaz-Martínez | Jairo Orozco-Sandoval | Vidya Manian | Balpreet K. Dhatt | Harkamal Walia
Source: Sensors (article, 2023-04-28)
Field: Chemistry — Analytical Chemistry

A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars. Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Preprocessor ; Pattern recognition (psychology) ; Deep learning ; Computer science ; Pixel ; Artificial neural network Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI
Authors: Krishnendu Mukherjee | Etinosa Osaro | Yamil J. Colón
Source: Digital Discovery (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF We present the development of an active learning framework to model multicomponent gas adsorption in metal–organic frameworks. Component (thermodynamics) ; Adsorption ; Computer science ; Materials science ; Chemistry ; Environmental science ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Maha N. Abu Hajleh | Muhamad O. Al-limoun | Amjad Al-Tarawneh | Tahani Hijazin | Moath Alqaraleh | Khaled M. Khleifat | Osama Al‐Madanat | Yaseen Al Qaisi | Ahmad Alsarayreh | Ali Al‐Samydai | Haitham Qaralleh | Emad A. S. Al‐Dujaili
Source: Molecules (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Synergistic Effects of AgNPs and Biochar: A Potential Combination for Combating Lung Cancer and Pathogenic Bacteria The synthesis of reliable biological nanomaterials is a crucial area of study in nanotechnology. In this study, Emericella dentata was employed for the biosynthesis of AgNPs, which were then combined with synthesized biochar, a porous structure created through biomass pyrolysis. The synergistic effects of AgNPs and biochar were evaluated through the assessment of pro-inflammatory cytokines, anti-apoptotic gene expression, and antibacterial activity. Solid biosynthesized AgNPs were evaluated by XRD and SEM, with SEM images revealing that most of the AgNPs ranged from 10 to 80 nm, with over 70% being less than 40 nm. FTIR analysis indicated the presence of stabilizing and reducing functional groups in the AgNPs. The nanoemulsion's zeta potential, hydrodynamic diameter, and particle distribution index were found to be -19.6 mV, 37.62 nm, and 0.231, respectively. Biochar, on the other hand, did not have any antibacterial effects on the tested bacterial species. However, when combined with AgNPs, its antibacterial efficacy against all bacterial species was significantly enhanced. Furthermore, the combined material significantly reduced the expression of anti-apoptotic genes and pro-inflammatory cytokines compared to individual treatments. This study suggests that low-dose AgNPs coupled with biochar could be a more effective method to combat lung cancer epithelial cells and pathogenic bacteria compared to either substance alone. Biochar ; Zeta potential ; Chemistry ; Pathogenic bacteria ; Antibacterial activity ; Nuclear chemistry ; Pyrolysis ; Apoptosis ; Bacteria ; Biomass (ecology) ; Nanomaterials ; Silver nanoparticle ; Fourier transform infrared spectroscopy ; Microbiology ; Nanotechnology ; Chemical engineering ; Materials science ; Biochemistry ; Nanoparticle ; Organic chemistry ; Biology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications