jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Haripriyaa | K. Suthindhiran
Source: Future Journal of Pharmaceutical Sciences (article, 2023-12-11)
Field: Materials Science — Biomaterials

Pharmacokinetics of nanoparticles: current knowledge, future directions and its implications in drug delivery Abstract Background Nanoparticles have emerged as a viable biological candidate with the possibility to be employed as drug carriers. They acquire high surface-to-volume ratios and unique physicochemical features such as biochemical, magnetic, optical, and electrical changes at the cellular, atomic, and molecular levels. This phenomenon has proven extensive utility for biomedical applications, as their biological activity has fewer adverse effects than traditional medications. Main body of the abstract The new spectrum of nanomaterials—nanomedicines—has accomplished disease management by detecting, restoring, and regeneration of damaged tissues. Therefore, designing appropriate nanomaterial-based drug delivery systems for final clinical evaluations requires accurate knowledge of pharmacokinetic factors relevant to the LADME in order to meet the required criteria (liberation, adsorption, distribution, metabolism, and elimination). To identify and predict the in vivo reaction of nanoparticles, a deeper understanding of the link between the physicochemical properties of nanomaterials and their contact with the body is necessary. This will allow a distinguished comparison of traditional medicines and nanoparticles. Short conclusion This review paper attempts to analyze the basic pharmacokinetic potential of nanoparticles in depth. Therefore, profiling the pharmacokinetic analysis will enable us to review the treatment outcome to overcome their adverse properties, provide a broad overview, and deliver remarkable ways to advance the use of nanoparticles in the biomedical industry. Graphical abstract Nanotechnology ; Drug delivery ; Pharmacokinetics ; Nanoparticle ; Drug ; Nanomaterials ; Biological fluids ; Biochemical engineering ; Computer science ; Risk analysis (engineering) ; Chemistry ; Pharmacology ; Materials science ; Medicine ; Engineering Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Advanced Drug Delivery Systems
Authors: Hendrik Jung | Roberto Covino | A. Arjun | Christian Leitold | Christoph Dellago | Peter G. Bolhuis | Gerhard Hummer
Source: Nature Computational Science (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Machine-guided path sampling to discover mechanisms of molecular self-organization Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Here we present an autonomous path sampling algorithm that integrates deep learning and transition path theory to discover the mechanism of molecular self-organization phenomena. The algorithm uses the outcome of newly initiated trajectories to construct, validate and-if needed-update quantitative mechanistic models. Closing the learning cycle, the models guide the sampling to enhance the sampling of rare assembly events. Symbolic regression condenses the learned mechanism into a human-interpretable form in terms of relevant physical observables. Applied to ion association in solution, gas-hydrate crystal formation, polymer folding and membrane-protein assembly, we capture the many-body solvent motions governing the assembly process, identify the variables of classical nucleation theory, uncover the folding mechanism at different levels of resolution and reveal competing assembly pathways. The mechanistic descriptions are transferable across thermodynamic states and chemical space. Mechanism (biology) ; Computer science ; Sampling (signal processing) ; Path (computing) ; Construct (python library) ; Process (computing) ; Artificial intelligence ; Observable ; Folding (DSP implementation) ; Physics ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Manuel Matzinger | Elisabeth Müller | Gerhard Dürnberger | Peter Pichler | Karl Mechtler
Source: Analytical Chemistry (article, 2023-02-20)
Field: Chemistry — Spectroscopy

Robust and Easy-to-Use One-Pot Workflow for Label-Free Single-Cell Proteomics The analysis of ultralow input samples or even individual cells is essential to answering a multitude of biomedical questions, but current proteomic workflows are limited in their sensitivity and reproducibility. Here, we report a comprehensive workflow that includes improved strategies for all steps, from cell lysis to data analysis. Thanks to convenient-to-handle 1 μL sample volume and standardized 384-well plates, the workflow is easy for even novice users to implement. At the same time, it can be performed semi-automatized using CellenONE, which allows for the highest reproducibility. To achieve high throughput, ultrashort gradient lengths down to 5 min were tested using advanced μ-pillar columns. Data-dependent acquisition (DDA), wide-window acquisition (WWA), data-independent acquisition (DIA), and commonly used advanced data analysis algorithms were benchmarked. Using DDA, 1790 proteins covering a dynamic range of four orders of magnitude were identified in a single cell. Using DIA, proteome coverage increased to more than 2200 proteins identified from single-cell level input in a 20 min active gradient. The workflow enabled differentiation of two cell lines, demonstrating its suitability to cellular heterogeneity determination. Workflow ; Reproducibility ; Chemistry ; Proteome ; Proteomics ; Data acquisition ; Throughput ; Lysis ; Computer science ; Data mining ; Biological system ; Chromatography ; Database ; Biochemistry Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cell Image Analysis Techniques
Authors: Daming Zeng | Weiping Deng | Xuefeng Jiang
Source: National Science Review (review, 2023-04-29)
Field: Chemistry — Organic Chemistry

Advances in the construction of diverse SuFEx linkers Sulfur fluoride exchange (SuFEx), a new generation of click chemistry, was first presented by Sharpless, Dong and co-workers in 2014. Owing to the high stability and yet efficient reactivity of the S<sup>VI</sup>-F bond, SuFEx has found widespread applications in organic synthesis, materials science, chemical biology and drug discovery. A diverse collection of SuFEx linkers has emerged, involving gaseous SO<sub>2</sub>F<sub>2</sub> and SOF<sub>4</sub> hubs; SOF<sub>4</sub>-derived iminosulfur oxydifluorides; <i>O</i>-, <i>N</i>- and <i>C</i>-attached sulfonyl fluorides and sulfonimidoyl fluorides; and novel sulfondiimidoyl fluorides. This review summarizes the progress of these SuFEx connectors, with an emphasis on analysing the advantages and disadvantages of synthetic strategies of these connectors based on the SuFEx concept, and it is expected to be beneficial to researchers to rapidly and correctly understand this field, thus inspiring further development in SuFEx chemistry. Reactivity (psychology) ; Nanotechnology ; Sulfur ; Combinatorial chemistry ; Drug discovery ; Chemistry ; Organic chemistry ; Materials science ; Biochemistry Click Chemistry and Applications ; Fluorine in Organic Chemistry ; Chemical Synthesis and Analysis
Authors: Xinyuan Bi | Li Lin | Zhou Chen | Jian Ye
Source: Small Methods (review, 2023-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track. Computer science ; Nanotechnology ; Artificial intelligence ; Pipeline (software) ; Data science ; Preprocessor ; Surface-enhanced Raman spectroscopy ; Systems engineering ; Biochemical engineering ; Raman spectroscopy ; Engineering ; Materials science ; Raman scattering ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jun Xia | Chengshuai Zhao | Bozhen Hu | Zhangyang Gao | Cheng Tan | Yue Liu | Siyuan Li | Stan Z. Li
Source: ChemRxiv (preprint, 2023-04-12)
Field: Materials Science — Materials Chemistry

Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs) for molecules. Typically, atom types as node attributes are randomly masked and GNNs are then trained to predict masked types as in AttrMask \citep{hu2020strategies}, following the Masked Language Modeling (MLM) task of BERT~\citep{devlin2019bert}. However, unlike MLM where the vocabulary is large, the AttrMask pre-training does not learn informative molecular representations due to small and unbalanced atom `vocabulary'. To amend this problem, we propose a variant of VQ-VAE~\citep{van2017neural} as a context-aware tokenizer to encode atom attributes into chemically meaningful discrete codes. This can enlarge the atom vocabulary size and mitigate the quantitative divergence between dominant (e.g., carbons) and rare atoms (e.g., phosphorus). With the enlarged atom `vocabulary', we propose a novel node-level pre-training task, dubbed Masked Atoms Modeling (MAM), to mask some discrete codes randomly and then pre-train GNNs to predict them. MAM also mitigates another issue of AttrMask, namely the negative transfer. It can be easily combined with various pre-training tasks to improve their performance. Furthermore, we propose triplet masked contrastive learning (TMCL) for graph-level pre-training to model the heterogeneous semantic similarity between molecules for effective molecule retrieval. MAM and TMCL constitute a novel pre-training framework, Mole-BERT, which can match or outperform state-of-the-art methods in a fully data-driven manner. We release the code at \textcolor{magenta}{\url{https://github.com/junxia97/Mole-BERT}}. Vocabulary ; Computer science ; Context (archaeology) ; Graph ; Node (physics) ; Artificial neural network ; Natural language processing ; Theoretical computer science ; Artificial intelligence ; Linguistics ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jake Burner | Jun Luo | Andrew J. P. White | Adam Mirmiran | Ohmin Kwon | Peter G. Boyd | Steven M. Maley | Marco Gibaldi | Scott Simrod | Victoria Ogden | Tom K. Woo
Source: Chemistry of Materials (article, 2023-01-20)
Field: Chemistry — Inorganic Chemistry

ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning Metal–organic frameworks (MOFs) are a class of crystalline materials composed of metal nodes or clusters connected via semi-rigid organic linkers. Owing to their high-surface area, porosity, and tunability, MOFs have received significant attention for numerous applications such as gas separation and storage. Atomistic simulations and data-driven methods [e.g., machine learning (ML)] have been successfully employed to screen large databases and successfully develop new experimentally synthesized and validated MOFs for CO2 capture. To enable data-driven materials discovery for any application, the first (and arguably most crucial) step is database curation. This work introduces the ab initio REPEAT charge MOF (ARC–MOF) database. This is a database of ∼280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. A key feature of ARC–MOF is that it contains density functional theory-derived electrostatic potential fitted partial atomic charges for each MOF. Additionally, ARC–MOF contains pre-computed descriptors for out-of-the-box ML applications. An in-depth analysis of the diversity of ARC–MOF with respect to the currently mapped design space of MOFs was performed─a critical, yet commonly overlooked aspect of previously reported MOF databases. Using this analysis, balanced subsets from ARC–MOF for various ML purposes have been identified, with a case study of the effect of training set on the ML performance. Other chemical and geometric diversity analyses are presented, with an analysis on the effect of the charge-assignment method on atomistic simulation of the gas uptake in MOFs. Metal-organic framework ; Database ; Arc (geometry) ; Porosity ; Materials science ; Ab initio ; Density functional theory ; Key (lock) ; Computer science ; Nanotechnology ; Chemistry ; Computational chemistry ; Geometry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Lingxiao Li | Jinfei Wei | Junping Zhang | Bucheng Li | Yanfei Yang | Jiaojiao Zhang
Source: Science Advances (review, 2023-10-20)
Field: Materials Science — Surfaces, Coatings and Films

Challenges and strategies for commercialization and widespread practical applications of superhydrophobic surfaces Superhydrophobic (SH) surfaces have progressed rapidly in fundamental research over the past 20 years, but their practical applications lag far behind. In this perspective, we first present the findings of a survey on the current state of SH surfaces including fundamental research, patenting, and commercialization. On the basis of the survey and our experience, this perspective explores the challenges and strategies for commercialization and widespread practical applications of SH surfaces. The comprehensive performances, preparation methods, and application scenarios of SH surfaces are the major constraints. These challenges should be addressed simultaneously, and the actionable strategies are provided. We then highlight the standard test methods of the comprehensive performances including mechanical stability, impalement resistance, and weather resistance. Last, the prospects of SH surfaces in the future are discussed. We anticipate that SH surfaces may be widely commercialized and used in practical applications around the year 2035 through combination of the suggested strategies and input from both academia and industry. Commercialization ; Perspective (graphical) ; Nanotechnology ; Computer science ; Resistance (ecology) ; Business ; Materials science ; Marketing ; Artificial intelligence ; Biology ; Ecology Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Fluid Dynamics and Heat Transfer
Authors: Prince Jain | Himanshu Chhabra | Urvashi Chauhan | Krishna Prakash | Akash Gupta | Mohamed S. Soliman | Md. Shabiul Islam | Mohammad Tariqul Islam
Source: Scientific Reports (article, 2023-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning assisted hepta band THz metamaterial absorber for biomedical applications A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest operational frequency, with multiple absorption peaks at 1.89, 4.15, 5.32, 5.84, 7.04, 8.02, and 8.13 THz. The impedance matching theory and electric field distribution are investigated to understand the physical mechanism of hepta-band absorption. The sensing functionality is evaluated using a surrounding medium with a refractive index between 1 and 1.1, resulting in good Quality factor (Q) value of 117. The proposed sensor has the highest sensitivity of 4.72 THz/RIU for glucose detection. Extreme randomized tree (ERT) model is utilized to predict absorptivities for intermediate frequencies with unit cell dimensions, substrate thickness, angle variation, and refractive index values to reduce simulation time. The effectiveness of the ERT model in predicting absorption values is evaluated using the Adjusted R<sup>2</sup> score, which is close to 1.0 for n<sub>min</sub> = 2, demonstrating the prediction efficiency in various test cases. The experimental results show that 60% of simulation time and resources can be saved by simulating absorber design using the ERT model. The proposed MMA sensor with an ERT model has potential applications in biomedical fields such as bacterial infections, malaria, and other diseases. Terahertz radiation ; Metamaterial absorber ; Resonator ; Refractive index ; Materials science ; Optoelectronics ; Metamaterial ; Multi-band device ; Substrate (aquarium) ; Absorption (acoustics) ; Polarization (electrochemistry) ; Sensitivity (control systems) ; Electric field ; Optics ; Computer science ; Electronic engineering ; Physics ; Telecommunications ; Composite material ; Chemistry Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Animal Vocal Communication and Behavior
Authors: Moustafa Mahmoud Yousry Zaghloul | Mai Mahmoud Yousry Zaghloul | Mohammed Fuseini
Source: Polymers for Advanced Technologies (review, 2023-07-26)
Field: Materials Science — Polymers and Plastics

Recent progress in Epoxy Nanocomposites: Corrosion, structural, flame retardancy and applications — A comprehensive review Abstract In the past few years, epoxy resins utilization has gained much significant recognition from researchers worldwide as a result of its advantages in different sectors such as automotive, aerospace, electronic systems, constructions, and other related fields because of its outstanding mechanical performance, corrosion protection, and dielectric properties. The nanomaterials incorporation into the matrix has been duly examined to be the most productive route to enhance polymer composite's mechanical properties. Despite that, the inherent brittleness, relatively low fracture toughness, cross‐linking ability, poor electrical, and thermal properties of epoxy render it weak to growth and initiation of cracks, limiting its utilization in state‐of‐the‐art structural applications. Especially, epoxy is flammable while releasing a considerable amount of gases and smoke, thus, extending a potential risk to lives. Consequently, several nanofiller materials like graphene (Gr), transition metal dichalcogenides, MXene, and hexagonal boron nitride (h‐BN) produce extensive chances to equip multi‐functional characteristics and then reinforce the epoxy resins for the state‐of‐the‐art application. With this current review, the present literature investigation of epoxy filled with nano‐sized material has been examined thoroughly. Current advances in the approach of integrating nanosized materials in the epoxy matrices have been also introduced. Most significantly, anti‐corrosion, mechanical, and flame‐retardant properties of nanomaterials reinforced epoxy nanocomposites have also been reviewed in particular. Ultimately, the present statuses of the field in addition to the future approach have been considered concerning the usefulness of numerous nanofillers toward reinforcement of epoxy in construction and building materials. The expectation in this extensive review could produce a valuable reference, ingenuity, and guidance for researchers in this area of study. Epoxy ; Materials science ; Brittleness ; Fracture toughness ; Nanocomposite ; Nanomaterials ; Toughness ; Boron nitride ; Composite material ; Fire retardant ; Corrosion ; Nanotechnology Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Electromagnetic wave absorption materials
Authors: Marzena Symonowicz | Mateusz Kolanek
Source: TU repository (Lodz University of Technology) (article, 2023-01-30)
Field: Chemistry — Organic Chemistry

Flavonoids and their properties to form chelate complexes The medical properties of naturally occurring compounds such as flavonoids have been well known for many years. However, the discovery that complexes of flavonoids with metal ions are more effective than free flavonoids changed the course of chemistry research. Flavonoids can be effective drugs because of their potent chelating or neuroprotective, radical scavenging and anti-inflammatory properties. Our paper is a literature review about flavonoids and their complexes which are generally used in medicine and pharmacy. Chelation ; Chemistry ; Computer science ; Organic chemistry Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Genomics, phytochemicals, and oxidative stress
Authors: Rupali Nagar | S. K. Srivastava | Sterlin Leo Hudson | Sandra Amaya | Ashish R. Tanna | Meenu Sharma | A. Ramesh | Sanjiv Sonkaria | Varsha Khare | Sesha S. Srinivasan
Source: Solar Compass (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

Recent developments in state-of-the-art hydrogen energy technologies – Review of hydrogen storage materials Hydrogen energy has been assessed as a clean and renewable energy source for future energy demand. For harnessing hydrogen energy to its fullest potential, storage is a key parameter. It is well known that important hydrogen storage characteristics are operating pressure-temperature of hydrogen, hydrogen storage capacity, hydrogen absorption-desorption kinetics and heat transfer in the hydride bed. Each application needs specific properties. Every class of hydrogen storage materials has a different set of hydrogenation characteristics. Hence, it is required to understand the properties of all hydrogen storage materials. The present review is focused on the state-of–the–art hydrogen storage materials including metal hydrides, magnesium-based materials, complex hydride systems, carbonaceous materials, metal organic frameworks, perovskites and materials and processes based on artificial intelligence. In each category of materials’ discovery, hydrogen storage mechanism and reaction, crystal structure and recent progress have been discussed in detail. Together with the fundamental synthesis process, latest techniques of material tailoring like nanostructuring, nanoconfinement, catalyzing, alloying and functionalization have also been discussed. Hydrogen energy research has a promising potential to replace fossil fuels from energy uses, especially from automobile sector. In this context, efforts initiated worldwide for clean hydrogen production and its use via fuel cell in vehicles is much awaiting steps towards sustainable energy demand. Hydrogen storage ; Hydrogen ; Hydrogen fuel ; Hydride ; Materials science ; Hydrogen economy ; Energy storage ; Fossil fuel ; Hydrogen technologies ; Hydrogen production ; Context (archaeology) ; Renewable energy ; Energy carrier ; Process engineering ; Nanotechnology ; Waste management ; Chemistry ; Engineering ; Organic chemistry ; Thermodynamics Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Nanomaterials for catalytic reactions