jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haotian Wang | Fei Xu | Lu Cai | Daniele Salvato | Fidelma Giulia Di Lemma | Luca Capriotti | Tiankai Yao | Min Xian
Source: Scientific Reports (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model's performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design. Computer science ; Convolutional neural network ; Porosity ; Magnification ; Scanning electron microscope ; Cladding (metalworking) ; Deep learning ; Workflow ; Materials science ; Artificial intelligence ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Daniel Muñoz-Escudero | Steven D. Breazeale | Myeongseon Lee | Ziqiang Guan | Christian R.H. Raetz | Marcelo C. Sousa
Source: Biochemistry (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Structure and Function of ArnD. A Deformylase Essential for Lipid A Modification with 4-Amino-4-deoxy-<scp>l</scp>-arabinose and Polymyxin Resistance Covalent modification of lipid A with 4-deoxy-4-amino-l-arabinose (Ara4N) mediates resistance to cationic antimicrobial peptides and polymyxin antibiotics in Gram-negative bacteria. The proteins required for Ara4N biosynthesis are encoded in the <i>pmrE</i> and <i>arnBCADTEF</i> loci, with ArnT ultimately transferring the amino sugar from undecaprenyl-phospho-4-deoxy-4-amino-l-arabinose (C55P-Ara4N) to lipid A. However, Ara4N is N-formylated prior to its transfer to undecaprenyl-phosphate by ArnC, requiring a deformylase activity downstream in the pathway to generate the final C55P-Ara4N donor. Here, we show that deletion of the <i>arnD</i> gene in an <i>Escherichia coli</i> mutant that constitutively expresses the <i>arnBCADTEF</i> operon leads to accumulation of the formylated ArnC product undecaprenyl-phospho-4-deoxy-4-formamido-l-arabinose (C55P-Ara4FN), suggesting that ArnD is the downstream deformylase. Purification of <i>Salmonella typhimurium</i> ArnD (stArnD) shows that it is membrane-associated. We present the crystal structure of stArnD revealing a NodB homology domain structure characteristic of the metal-dependent carbohydrate esterase family 4 (CE4). However, ArnD displays several distinct features: a 44 amino acid insertion, a C-terminal extension in the NodB fold, and sequence divergence in the five motifs that define the CE4 family, suggesting that ArnD represents a new family of carbohydrate esterases. The insertion is responsible for membrane association as its deletion results in a soluble ArnD variant. The active site retains a metal coordination H-H-D triad, and in the presence of Co<sup>2+</sup> or Mn<sup>2+</sup>, purified stArnD efficiently deformylates C55P-Ara4FN confirming its role in Ara4N biosynthesis. Mutations D9N and H233Y completely inactivate stArnD implicating these two residues in a metal-assisted acid-base catalytic mechanism. Biochemistry ; Operon ; Amino acid ; Biology ; Chemistry ; Mutant ; Gene Enzyme Structure and Function ; Antibiotic Resistance in Bacteria ; Biochemical and Molecular Research
Authors: Guomin Zhang | Vipul Mann | Venkat Venkatasubramanian
Source: AIChE Journal (article, 2023-10-12)
Field: Materials Science — Materials Chemistry

G‐MATT: Single‐step retrosynthesis prediction using molecular grammar tree transformer Abstract Various template‐based and template‐free approaches have been proposed for single‐step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data‐driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry‐aware retrosynthesis prediction framework that combines powerful data‐driven models with prior domain knowledge. We present a tree‐to‐sequence transformer architecture that utilizes hierarchical SMILES grammar‐based trees, incorporating crucial chemistry information that is often overlooked by SMILES text‐based representations, such as local structures and functional groups. The proposed framework, grammar‐based molecular attention tree transformer (G‐MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G‐MATT achieves a promising top‐1 accuracy of 51% (top‐10 accuracy of 79.1%), an invalid rate of 1.5%, and a bioactive similarity rate of 74.8% on the USPTO‐50K dataset. Additional analyses of G‐MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures. Retrosynthetic analysis ; Transformer ; Grammar ; Computer science ; Artificial intelligence ; Tree (set theory) ; Natural language processing ; Machine learning ; Chemistry ; Engineering ; Linguistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Diego Iván Caviedes Rubio | Claudia Patricia Ortíz | Fleming Martínez | Daniel Ricardo Delgado
Source: Molecules (article, 2023-10-22)
Field: Materials Science — Materials Chemistry

Thermodynamic Assessment of Triclocarban Dissolution Process in N-Methyl-2-pyrrolidone + Water Cosolvent Mixtures Solubility is one of the most important physicochemical properties due to its involvement in physiological (bioavailability), industrial (design) and environmental (biotoxicity) processes, and in this regard, cosolvency is one of the best strategies to increase the solubility of poorly soluble drugs in aqueous systems. Thus, the aim of this research is to thermodynamically evaluate the dissolution process of triclocarban (TCC) in cosolvent mixtures of {<i>N</i>-methyl-2-pyrrolidone (NMP) + water (W)} at seven temperatures (288.15, 293.15, 298.15, 303.15, 308.15, 313.15 and 318.15 K). Solubility is determined by UV/vis spectrophotometry using the flask-shaking method. The dissolution process of the TCC is endothermic and strongly dependent on the cosolvent composition, achieving the minimum solubility in pure water and the maximum solubility in NMP. The activity coefficient decreases from pure water to NMP, reaching values less than one, demonstrating the excellent positive cosolvent effect of NMP, which is corroborated by the negative values of the Gibbs energy of transfer. In general terms, the dissolution process is endothermic, and the increase in TCC solubility may be due to the affinity of TCC with NMP, in addition to the water de-structuring capacity of NMP generating a higher number of free water molecules. Solubility ; Dissolution ; Endothermic process ; Chemistry ; Triclocarban ; Gibbs free energy ; Aqueous solution ; COSMO-RS ; Molar solubility ; Solubility equilibrium ; Bioavailability ; Organic chemistry ; Thermodynamics ; Ionic liquid ; Adsorption Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Drug Solubulity and Delivery Systems
Authors: Szabolcs Góger | Leonardo Medrano Sandonas | Carolin Müller | Alexandre Tkatchenko
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Data-driven tailoring of molecular dipole polarizability and frontier orbital energies in chemical compound space Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and the dipole polarizability. Based on the recently introduced QM7-X dataset, augmented with accurate molecular polarizability calculations as well as analysis of functional group compositions, we show that polarizability and HOMO-LUMO gap are uncorrelated when considering sufficiently extended subsets of the chemical compound space. The relation between these two properties is further analyzed on specific examples of molecules with similar composition as well as homooligomers. Remarkably, the freedom brought by the lack of correlation between molecular polarizability and HOMO-LUMO gap enables the design of novel materials, as we demonstrate on the example of organic photodetector candidates. Polarizability ; HOMO/LUMO ; Dipole ; Uncorrelated ; Chemical space ; Space (punctuation) ; Molecular orbital ; Chemical physics ; Computational chemistry ; Molecular physics ; Materials science ; Physics ; Chemistry ; Molecule ; Quantum mechanics ; Computer science ; Drug discovery ; Mathematics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Jan P. F. Lagerwall
Source: Liquid Crystals (article, 2023-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The good, the bad and the ugly faces of cyanobiphenyl mesogens in selected tracks of fundamental and applied liquid crystal research Liquid crystal-forming cyanobiphenyls are truly extraordinary molecules that have had an enormous impact on liquid crystal research and applications since they were first synthesised. This impact is, on the one hand, due to the exceptionally convenient physical properties of the main characters, 5CB and 8CB, allowing easy experiments at room temperature, as well as their commercial availability at reasonable cost. On the other hand, the cyanobiphenyl chemical structure leads to some quite peculiar characteristics in terms of organisation at the molecular scale, which are sometimes well recognised and even utilised, but often the awareness of these peculiarities is not strong. This perspective article reviews the use of cyanobiphenyls in making liquid crystal shells and liquid crystal core fibres, in sensing, as a medium for simultaneously aligning and dispersing carbon nanotubes, and as highly useful solvents for reactive mesogens that can be polymerised into anisotropic networks. This choice is very much motivated by how cyanobiphenyls have impacted our group's research throughout the years, which is the basis for the examples I provide. Nevertheless, I believe they serve well to illustrate the immense usefulness of cyanobiphenyls in innovating research and applications related to liquid crystals. Liquid crystal ; Materials science ; Crystal (programming language) ; Molecule ; Carbon nanotube ; Perspective (graphical) ; Nanotechnology ; Chemical physics ; Organic chemistry ; Optoelectronics ; Chemistry ; Computer science Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Synthesis and Properties of Aromatic Compounds
Authors: K. Deeba | O. Rama Devi | Mohammed Saleh Al Ansari | Bhargavi Peddi Reddy | H T Manohara | Yousef A. Baker El–Ebiary | Manikandan Rengarajan
Source: International Journal of Advanced Computer Science and Applications (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning The optimization of crop yield projections has arisen as a major problem in modern agriculture, due to the increasing demand for food supply and the necessity for effective resource management. Precision and scalability are hampered by the limits associated with conventional agricultural production prediction techniques, which mostly rely on observations and simple data sources. While methods like random forest (RF) and K-nearest neighbors (KNN) are widely used, their reliance on personal assessments and insufficient knowledge of crop attributes typically results in less accurate forecasts and makes them unsuitable for agricultural precision. The suggested method combines deep learning, spectral unmixing, and hyperspectral imaging methods to overcome these obstacles. With the use of hyperspectral imaging, which records a vast array of data that is not visible to the human eye, crop attributes may be thoroughly examined and can identify the unique spectral fingerprints of different agricultural constituents by using spectral unmixing approaches, which makes it easier to evaluate the health and growth phases of the crop. Then, using this augmented spectral data, deep learning algorithms create a solid, data-driven basis for precise crop production prediction. MATLAB has been used in the suggested workflow. The combination of deep learning, spectrum unmixing, and hyperspectral imaging provides a comprehensive, cutting-edge approach that goes beyond the constraints of conventional techniques were implemented in python. Some of the algorithms that were examined, this one with integration has the lowest Root Mean Square Error (RMSE) of 0.15 and Mean Absolute Error (MAE) of 0.14, demonstrating higher prediction accuracy above other current models. This novel method represents a substantial breakthrough in precision agriculture while also improving crop production prediction. Hyperspectral imaging ; Computer science ; Python (programming language) ; Artificial intelligence ; Deep learning ; Precision agriculture ; Scalability ; Random forest ; Mean squared error ; Machine learning ; Crop yield ; Pattern recognition (psychology) ; Agriculture ; Mathematics ; Statistics ; Database Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Dereje Seifu | Qing Peng | Kit Sze | Jie Hou | Fei Gao | Yucheng Lan
Source: Molecules (review, 2023-05-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic Radiation Effects on MgO-Based Magnetic Tunnel Junctions: A Review Magnetic tunnel junctions (MTJs) have been widely utilized in sensitive sensors, magnetic memory, and logic gates due to their tunneling magnetoresistance. Moreover, these MTJ devices have promising potential for renewable energy generation and storage. Compared with Si-based devices, MTJs are more tolerant to electromagnetic radiation. In this review, we summarize the functionalities of MgO-based MTJ devices under different electromagnetic irradiation environments, with a focus on gamma-ray radiation. We explore the effects of these radiation exposures on the MgO tunnel barriers, magnetic layers, and interfaces to understand the origin of their tolerance. This review enhances our knowledge of the radiation tolerance of MgO-based MTJs, improves the design of these MgO-based MTJ devices with better tolerances, and provides information to minimize the risks of irradiation under various irradiation environments. This review starts with an introduction to MTJs and irradiation backgrounds, followed by the fundamental properties of MTJ materials, such as the MgO barrier and magnetic layers. Then, we review and discuss the MTJ materials and devices' radiation tolerances under different irradiation environments, including high-energy cosmic radiation, gamma-ray radiation, and lower-energy electromagnetic radiation (X-ray, UV-vis, infrared, microwave, and radiofrequency electromagnetic radiation). In conclusion, we summarize the radiation effects based on the published literature, which might benefit material design and protection. Radiation ; Electromagnetic radiation ; Materials science ; Engineering physics ; Environmental science ; Optoelectronics ; Physics ; Optics Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films ; ZnO doping and properties
Authors: Aminur Rahman | Harunur Rasid | Md. Isahak Ali | Nymul Yeachin | MS Alam | Khandker Saadat Hossain | Md. Abdul Kafi
Source: Journal of Nanotechnology (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Facile Synthesis and Application of Ag-NPs for Controlling Antibiotic-ResistantPseudomonas spp. and Bacillus spp. in a Poultry Farm Environment This study synthesized silver nanoparticles (Ag-NPs) using silver nitrate (AgNO3) as the ion source and sodium tripolyphosphate (STPP) as reducing as well as capping agents. The synthesized Ag-NPs were confirmed initially using Ag-NPs specific λmax at 410 nm with UV-Vis spectrophotometry and homogenously distributed, 100–300 nm size, and round-shaped particles were realized through atomic force microscopy (AFM) and transmission electron microscopy (TEM) image analysis. The various reaction condition-based studies revealed 0.01 M AgNO3 yields maximum particle after 4 h reduction with 1% STPP. Bacillus spp. (n = 23/90) and Pseudomonas spp. (n = 26/90) were isolated from three different poultry farms for evaluating the antibacterial activity of Ag-NPs. Among the PCR confirmed isolates, 52% (12/23) Bacillus spp. were resistant to ten antibiotics and 65% (17/26) Pseudomonas spp. were resistant to eleven antibiotics. The representative resistant isolates were subjected to antibacterial evaluation of synthesized Ag-NPs following the well diffusion method, revealing the maximum sensitive zone of inhibition 19 ± 0.2 mm against Bacillus spp. and 17 ± 0.38 mm against Pseudomonas spp. The minimum inhibitory concentration (MIC) and minimum bacterial concentration (MBC) of Ag-NPs were 2.1 μg/ml and 8.4 μg/ml, respectively, for broad-spectrum application. Finally, the biocompatibility was determined by observing the viability of Ag-NP-treated BHK-21 cell through trypan blue-based exclusion assay revealing nonsignificant decreased of cell viability ≤2MIC doses. Thus, the synthesized Ag-NPs were proven as biocompatible and sensitive to both Gram-positive and Gram-negative bacteria of the poultry farm environmental samples. Nuclear chemistry ; Trypan blue ; Biocompatibility ; Pseudomonas ; Antibacterial activity ; Materials science ; Transmission electron microscopy ; Viability assay ; Minimum inhibitory concentration ; Antimicrobial ; Agar diffusion test ; Bacteria ; Silver nanoparticle ; Microbiology ; Nanoparticle ; Nanotechnology ; Chemistry ; Biology ; Biochemistry ; Cell Nanoparticles: synthesis and applications ; Pharmaceutical and Antibiotic Environmental Impacts ; Antimicrobial agents and applications
Authors: Han Wang | Maya K. Monroe | Feihu Wang | Mingjiao Sun | Charles Flexner | Honggang Cui
Source: Journal of the American Chemical Society (article, 2023-09-25)
Field: Materials Science — Biomaterials

Constructing Antiretroviral Supramolecular Polymers as Long-Acting Injectables through Rational Design of Drug Amphiphiles with Alternating Antiretroviral-Based and Hydrophobic Residues One of the main challenges in the development of long-acting injectables for HIV treatment is the limited duration of drug release, which results in the need for frequent dosing and reduced patient adherence. In this context, we leverage the intrinsic reversible features of supramolecular polymers and their unique ability to form a three-dimensional network under physiological conditions to design a class of self-assembling drug amphiphiles (DAs) based upon lamivudine, a water-soluble antiretroviral (ARV) agent and nucleoside reverse transcriptase inhibitor. The designed ARV DAs contain three pairs of alternating hydrophobic valine (V) and hydrophilic lamivudine-modified lysine (K<sup>3TC</sup>) residues with a varying number of glutamic acids (E) placed on the <i>C</i>-terminus. Upon dissolution in deionized water, all three ARV DAs were found to spontaneously associate into supramolecular filaments of several micrometers in length, with varying levels of lateral stacking. Addition of 1× PBS triggered immediate gelation of the two ARV DAs with 2 or 3 E residues, and upon dilution in an <i>in vitro</i> setting, the dissociation from the supramolecular state to the monomeric state enabled a long-acting linear release of the ARV DAs. <i>In vivo</i> studies further confirmed their injectability, rapid <i>in situ</i> hydrogel formation, enhanced local retention, and long-acting therapeutic release over a month. Importantly, our pharmacokinetic studies suggest that the injected ARV supramolecular polymeric hydrogel was able to maintain a plasma concentration of lamivudine above its IC<sub>50</sub> value for more than 40 days in mice and showed minimal systemic immunogenicity. We believe that these results shed important light on the rational design of long-acting injectables using the drug-based molecular assembly strategy, and the reported ARV supramolecular hydrogels hold great promise for improving HIV treatment outcomes. Chemistry ; Supramolecular chemistry ; Lamivudine ; Amphiphile ; Supramolecular polymers ; Polymer ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Virus ; Molecule ; Copolymer ; Virology Supramolecular Self-Assembly in Materials ; HIV Research and Treatment ; RNA Interference and Gene Delivery
Authors: Seonik Lee | Mengchi Jiao | Zihan Zhang | Yan Yu
Source: Annual Review of Analytical Chemistry (review, 2023-06-14)
Field: Materials Science — Materials Chemistry

Nanoparticles for Interrogation of Cell Signaling Cell functions rely on signal transduction-the cascades of molecular interactions and biochemical reactions that relay extracellular signals to the cell interior. Dissecting principles governing the signal transduction process is critical for the fundamental understanding of cell physiology and the development of biomedical interventions. The complexity of cell signaling is, however, beyond what is accessible by conventional biochemistry assays. Thanks to their unique physical and chemical properties, nanoparticles (NPs) have been increasingly used for the quantitative measurement and manipulation of cell signaling. Even though research in this area is still in its infancy, it has the potential to yield new, paradigm-shifting knowledge of cell biology and lead to biomedical innovations. To highlight this importance, we summarize in this review studies that pioneered the development and application of NPs for cell signaling, from quantitative measurements of signaling molecules to spatiotemporal manipulation of cell signal transduction. Signal transduction ; Cell signaling ; Cell ; Transduction (biophysics) ; Cell biology ; Nanotechnology ; Biology ; Chemistry ; Biophysics ; Biochemistry ; Materials science Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications