jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suhel Parvez | Medha Kaushik | Mubashshir Ali | Mohammad Mumtaz Alam | Javed Ali | Heena Tabassum | Pooja Kaushik
Source: Theranostics (review, 2021-12-15)
Field: Materials Science — Materials Chemistry

Dodging blood brain barrier with “nano” warriors: Novel strategy against ischemic stroke Ischemic stroke (IS) is one of the leading causes of death and disability resulting in inevitable burden globally. Ischemic injury initiates cascade of pathological events comprising energy dwindling, failure of ionic gradients, failure of blood brain barrier (BBB), vasogenic edema, calcium over accumulation, excitotoxicity, increased oxidative stress, mitochondrial dysfunction, inflammation and eventually cell death. In spite of such complexity of the disease, the only treatment approved by US Food and Drug Administration (FDA) is tissue plasminogen activator (t-PA). This therapy overcome blood deficiency in the brain along with side effects of reperfusion which are responsible for considerable tissue injury. Therefore, there is urgent need of novel therapeutic perspectives that can protect the integrity of BBB and salvageable brain tissue. Advancement in nanomedicine is empowering new approaches that are potent to improve the understanding and treatment of the IS. Herein, we focus nanomaterial mediated drug delivery systems (DDSs) and their role to bypass and cross BBB especially via intranasal drug delivery. The various nanocarriers used in DDSs are also discussed. In a nut shell, the objective is to provide an overview of use of nanomedicine in the diagnosis and treatment of IS to facilitate the research from benchtop to bedside. Medicine ; Nanocarriers ; Blood–brain barrier ; Excitotoxicity ; Drug delivery ; Tissue plasminogen activator ; Nanomedicine ; Drug ; Oxidative stress ; Stroke (engine) ; Inflammation ; Pharmacology ; Neuroscience ; Bioinformatics ; Central nervous system ; Internal medicine ; Glutamate receptor ; Nanotechnology Advanced Nanomaterials in Catalysis ; Barrier Structure and Function Studies ; Acute Ischemic Stroke Management
Authors: Dhanapal Venkatachalam | Subramanian Kaliappa
Source: Reviews in Chemical Engineering (review, 2021-07-02)
Field: Materials Science — Polymers and Plastics

Superabsorbent polymers: a state-of-art review on their classification, synthesis, physicochemical properties, and applications Abstract Superabsorbent polymers (SAP) and modified natural polymer hydrogels are widely and increasingly used in agriculture, health care textiles, effluent treatment, drug delivery, tissue engineering, civil concrete structure, etc. However, not many comprehensive reviews are available on this class of novel polymers. A review covering all the viable applications of SAP will be highly useful for researchers, industry persons, and medical, healthcare, and agricultural purposes. Hence, an attempt has been made to review SAPs with reference to their classifications, synthesis, modification by crosslinking, and physicochemical characterization such as morphology, swellability, thermal and mechanical properties, lifetime prediction, thermodynamics of swelling, absorption, release and transport kinetics, quantification of hydrophilic groups, etc. Besides, the possible methods of fine-tuning their structures for improving their absorption capacity, fast absorption kinetics, mechanical strength, controlled release features, etc. were also addressed to widen their uses. This review has also highlighted the biodegradability, commercial viability and market potential of SAPs, SAP composites, the feasibility of using biomass as raw materials for SAP production, etc. The challenges and future prospects of SAP, their safety, and environmental issues are also discussed. Superabsorbent polymer ; Self-healing hydrogels ; Polymer ; Raw material ; Swelling ; Materials science ; Characterization (materials science) ; Biochemical engineering ; Biodegradation ; Process engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Polymer chemistry ; Engineering Polymer Nanocomposites and Properties ; Hydrogels: synthesis, properties, applications ; Polymer-Based Agricultural Enhancements
Authors: Saurav Das | Debapratim Das
Source: Frontiers in Chemistry (review, 2021-11-16)
Field: Materials Science — Biomaterials

Rational Design of Peptide-based Smart Hydrogels for Therapeutic Applications Peptide-based hydrogels have captivated remarkable attention in recent times and serve as an excellent platform for biomedical applications owing to the impressive amalgamation of unique properties such as biocompatibility, biodegradability, easily tunable hydrophilicity/hydrophobicity, modular incorporation of stimuli sensitivity and other functionalities, adjustable mechanical stiffness/rigidity and close mimicry to biological molecules. Putting all these on the same plate offers smart soft materials that can be used for tissue engineering, drug delivery, 3D bioprinting, wound healing to name a few. A plethora of work has been accomplished and a significant progress has been realized using these peptide-based platforms. However, designing hydrogelators with the desired functionalities and their self-assembled nanostructures is still highly serendipitous in nature and thus a roadmap providing guidelines toward designing and preparing these soft-materials and applying them for a desired goal is a pressing need of the hour. This review aims to provide a concise outline for that purpose and the design principles of peptide-based hydrogels along with their potential for biomedical applications are discussed with the help of selected recent reports. Self-healing hydrogels ; Nanotechnology ; Soft materials ; Drug delivery ; Biocompatibility ; Modular design ; Materials science ; Smart material ; Rational design ; Computer science Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Polydiacetylene-based materials and applications
Authors: Phillip M. Maffettone | Lars Banko | Peng Cui | Yury Lysogorskiy | Marc A. Little | Daniel Olds | Alfred Ludwig | Andrew I. Cooper
Source: Nature Computational Science (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Crystallography companion agent for high-throughput materials discovery Characterization (materials science) ; Automation ; Set (abstract data type) ; Probabilistic logic ; Phase (matter) ; Inverse Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Guang Chen | Lei Tao | Ying Li
Source: Polymers (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model We propose a chemical language processing model to predict polymers' glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of a polymer's repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point '*'. Results show that the trained model demonstrates reasonable prediction performance on unseen polymer's Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of polymer Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer properties. Polymer ; Computer science ; Glass transition ; Representation (politics) ; Embedding ; Language model ; Polymerization ; Construct (python library) ; Feature (linguistics) ; Biological system ; Point (geometry) ; Algorithm ; Artificial intelligence ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Hongkui Zheng | Xiner Lu | Kai He
Source: Journal of Energy Chemistry (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials Nanotechnology ; Materials science ; Transmission electron microscopy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yu Kumagai | Naoki Tsunoda | Akira Takahashi | Fumiyasu Oba
Source: Physical Review Materials (article, 2021-12-27)
Field: Materials Science — Materials Chemistry

Insights into oxygen vacancies from high-throughput first-principles calculations Oxygen vacancies play significant roles in various properties of oxide materials. Therefore, insights into the oxygen vacancies can facilitate the discovery of better oxide materials. To achieve this, we developed codes for high-throughput point-defect calculations and applied them to characterize oxygen vacancies in 937 oxides. From the resulting large dataset, we analyzed the vacancy structures and formation energies and constructed machine-learning regression models to predict vacancy formation energies. We have found that the vacancy formation energies are predicted using the random forest regression models with accuracies of 0.27--0.44 eV depending on the charge states. Analyses of the importance of the descriptors show that the formation energies of the neutral vacancies are mainly determined by the orbital characteristics of the conduction-band minima, the oxide stability, and the band gaps, whereas those of the doubly charged defects are determined by factors related to electrostatic energy. These codes and datasets are publicly available, and a graphical user interface is available to analyze the calculation results. Materials science ; Vacancy defect ; Oxide ; Oxygen ; Band gap ; Maxima and minima ; Chemical physics ; Crystallographic defect ; Conduction band ; Condensed matter physics ; Physics ; Optoelectronics ; Electron Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Chen Qu | Paul L. Houston | Riccardo Conte | Apurba Nandi | Joel M. Bowman
Source: The Journal of Physical Chemistry Letters (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

Breaking the Coupled Cluster Barrier for Machine-Learned Potentials of Large Molecules: The Case of 15-Atom Acetylacetone Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms are typically forced to use lower-level electronic structure methods such as density functional theory (DFT) and second-order Møller-Plesset perturbation theory (MP2). While these are efficient and realistic, they fall short of the accuracy of the "gold standard" coupled-cluster method, especially with respect to reaction and isomerization barriers. We report a major step forward in applying a Δ-machine learning method to the challenging case of acetylacetone, whose MP2 barrier height for H-atom transfer is low by roughly 1.1 kcal/mol relative to the benchmark CCSD(T) barrier of 3.2 kcal/mol. From a database of 2151 local CCSD(T) energies and training with as few as 430 energies, we obtain a new PES with a barrier of 3.5 kcal/mol in agreement with the LCCSD(T) barrier of 3.5 kcal/mol and close to the benchmark value. Tunneling splittings due to H-atom transfer are calculated using this new PES, providing improved estimates over previous ones obtained using an MP2-based PES. Acetylacetone ; Cluster (spacecraft) ; Atom (system on chip) ; Molecule ; Chemistry ; Atomic physics ; Chemical physics ; Physics ; Computer science ; Inorganic chemistry ; Organic chemistry ; Operating system Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Crystallography and molecular interactions
Authors: Hizkeal Tsade Kara | Sisay Tadesse Anshebo | Fedlu Kedir Sabir | Getachew Adam Workineh
Source: International Journal of Chemical Engineering (article, 2021-06-17)
Field: Materials Science — Biomaterials

Removal of Methylene Blue Dye from Wastewater Using Periodiated Modified Nanocellulose The study was focused on the preparation and characterizations of sodium periodate-modified nanocellulose (NaIO4-NC) prepared from Eichhornia crassipes for the removal of cationic methylene blue (MB) dye from wastewater (WW). A chemical method was used for the preparation of NaIO4-NC. The prepared NaIO4-NC adsorbent was characterized by using X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscope (SEM), energy-dispersive X-ray (EDX), and Brunauer–Emmett–Teller (BET) instruments. Next, it was tested to the adsorption of MB dye from WW using batch experiments. The adsorption process was performed using Langmuir and Freundlich isotherm models with maximum adsorption efficiency (qmax) of 90.91 mg·g−1 and percent color removal of 78.1% at optimum 30 mg·L−1, 60 min., 1 g, and 8 values of initial concentration, contact time, adsorbent dose, and solution pH, respectively. Pseudo-second-order (PSO) kinetic model was well fitted for the adsorption of MB dye through the chemisorption process. The adsorption process was spontaneous and feasible from the thermodynamic study because the Gibbs free energy value was negative. After adsorption, the decreased values for physicochemical parameters of WW were observed in addition to the color removal. From the regeneration study, it is possible to conclude that NaIO4-NC adsorbent was recyclable and reused as MB dye adsorption for 13 successive cycles without significant efficient loss. Adsorption ; Methylene blue ; Freundlich equation ; Chemisorption ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Chemistry ; Langmuir ; Scanning electron microscope ; Langmuir adsorption model ; Wastewater ; Materials science ; Cationic polymerization ; Gibbs free energy ; Chemical engineering ; Photocatalysis ; Organic chemistry ; Waste management ; Composite material Advanced Cellulose Research Studies ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Mihály Váradi | Stephen Anyango | David Armstrong | John M. Berrisford | Preeti Choudhary | Mandar Deshpande | Nurul Nadzirin | Sreenath Nair | Lukáš Pravda | Ahsan Tanweer | Bissan Al‐Lazikani | Claudia Andreini | Geoffrey J. Barton | David Bednář | Karel Berka | Tom L. Blundell | Kelly P. Brock | J.M. Carazo | Jiřı́ Damborský | Alessia David | Sucharita Dey | Roland L. Dunbrack | Juan Fernández Recio | Franca Fraternali | Toby J. Gibson | Manuela Helmer‐Citterich | David Hoksza | Thomas A. Hopf | Dávid Jakubec | Natarajan Kannan | Radoslav Krivák | Manjeet Kumar | Emmanuel D. Levy | Nir London | José Ramón Macías | Madhusudhan M Srivatsan | Debora S. Marks | Lennart Martens | Stuart A McGowan | Jake E McGreig | Vivek Modi | R. Gonzalo Parra | Gerardo Pepe | Damiano Piovesan | Jaime Prilusky | Valeria Putignano | Leandro Radusky | Pathmanaban Ramasamy | Atilio O Rausch | Nathalie Reuter | Luis A Rodriguez | Nathan Rollins | Antonio Rosato | Paweł Rubach | Luis Serrano | Gulzar Singh | Petr Škoda | Carlos Óscar S. Sorzano | Jan Štourač | Joanna I. Sułkowska | Radka Svobodová Vařeková | Natalia Tichshenko | Silvio C. E. Tosatto | Wim Vranken | Mark N. Wass | Dandan Xue | Daniel Zaidman | Janet M. Thornton | Michael J.E. Sternberg | Christine Orengo | Sameer Velankar
Source: Nucleic Acids Research (article, 2021-10-16)
Field: Materials Science — Materials Chemistry

PDBe-KB: collaboratively defining the biological context of structural data The Protein Data Bank in Europe - Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive. Biology ; Context (archaeology) ; Computational biology ; Genetics ; Paleontology Enzyme Structure and Function ; Protein Structure and Dynamics ; Bioinformatics and Genomic Networks
Authors: Daniel C. Castro | Yuxuan Richard Xie | Stanislav S. Rubakhin | Elena V. Romanova | Jonathan V. Sweedler
Source: Nature Methods (article, 2021-09-30)
Field: Chemistry — Spectroscopy

Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization Peptidergic dense-core vesicles are involved in packaging and releasing neuropeptides and peptide hormones-critical processes underlying brain, endocrine and exocrine function. Yet, the heterogeneity within these organelles, even for morphologically defined vesicle types, is not well characterized because of their small volumes. We present image-guided, high-throughput mass spectrometry-based protocols to chemically profile large populations of both dense-core vesicles and lucent vesicles for their lipid and peptide contents, allowing observation of the chemical heterogeneity within and between these two vesicle populations. The proteolytic processing products of four prohormones are observed within the dense-core vesicles, and the mass spectral features corresponding to the specific peptide products suggest three distinct dense-core vesicle populations. Notable differences in the lipid mass range are observed between the dense-core and lucent vesicles. These single-organelle mass spectrometry approaches are adaptable to characterize a range of subcellular structures. Vesicle ; Organelle ; Secretory Vesicle ; Mass spectrometry imaging ; Peptide ; Biology ; Mass spectrometry ; Biophysics ; Chemistry ; Cell biology ; Biochemistry ; Chromatography ; Membrane Mass Spectrometry Techniques and Applications ; Advanced biosensing and bioanalysis techniques ; Antimicrobial Peptides and Activities
Authors: Weijin Wang | Xinyao Li | Xiaoxue Yang | Lingsheng Ai | Zhiwen Gong | Ning Jiao | Song Song
Source: Nature Communications (article, 2021-06-23)
Field: Chemistry — Inorganic Chemistry

Oxoammonium salts are catalysing efficient and selective halogenation of olefins, alkynes and aromatics Electrophilic halogenation reactions have been a reliable approach to accessing organohalides. During the past decades, various catalytic systems have been developed for the activation of haleniums. However, there is still a short of effective catalysts, which could cover various halogenation reactions and broad scope of unsaturated compounds. Herein, TEMPO (2,2,6,6-tetramethylpiperidine nitroxide) and its derivatives are disclosed as active catalysts for electrophilic halogenation of olefins, alkynes, and aromatics. These catalysts are stable, readily available, and reactive enough to activate haleniums including Br<sup>+</sup>, I<sup>+</sup> and even Cl<sup>+</sup> reagents. This catalytic system is applicable to various halogenations including haloarylation of olefins or dibromination of alkynes, which were rarely realized in previous Lewis base catalysis or Lewis acid catalysis. The high catalytic ability is attributed to a synergistic activation model of electrophilic halogenating reagents, where the carbonyl group and the halogen atom are both activated by present TEMPO catalysis. Halogenation ; Electrophile ; Catalysis ; Chemistry ; Lewis acids and bases ; Organic chemistry ; Reagent ; Halogen ; Combinatorial chemistry ; Alkyl Vanadium and Halogenation Chemistry ; Chemical Synthesis and Reactions ; Sulfur-Based Synthesis Techniques
Authors: Kenneth S. Vecchio | Olivia F. Dippo | Kevin Kaufmann | Xiao Liu
Source: Acta Materialia (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

High-throughput rapid experimental alloy development (HT-READ) The current bulk materials discovery cycle has several inefficiencies from initial computational predictions through fabrication and analyses. Materials are generally evaluated in a singular fashion, relying largely on human-driven compositional choices and analysis of the volumes of generated data, thus also slowing validation of computational models. To overcome these limitations, we developed a high-throughput rapid experimental alloy development (HT-READ) methodology that comprises an integrated, closed-loop material screening process inspired by broad chemical assays and modern innovations in automation. Our method is a general framework unifying computational identification of ideal candidate materials, fabrication of sample libraries in a configuration amenable to multiple tests and processing routes, and analysis of the candidate materials in a high-throughput fashion. An artificial intelligence agent is used to find connections between compositions and material properties. New experimental data can be leveraged in subsequent iterations or new design objectives. The sample libraries are assigned unique identifiers and stored to make data and samples persistent, thus preventing institutional knowledge loss. Throughput ; Computer science ; Fabrication ; Identification (biology) ; Sample (material) ; Automation ; Materials science ; Process (computing) ; Identifier ; Process engineering ; Nanotechnology ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography