jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ioannis Papadimitriou | Ilias Gialampoukidis | Stefanos Vrochidis | Ioannis Kompatsiaris
Source: Computational Materials Science (review, 2024-01-21)
Field: Materials Science — Materials Chemistry

AI methods in materials design, discovery and manufacturing: A review Transformative learning ; Context (archaeology) ; Big data ; Data science ; Process (computing) ; Computer science ; Field (mathematics) ; Materials informatics ; Informatics ; Engineering ; Management science ; Nanotechnology ; Engineering informatics ; Health informatics ; Data mining ; Materials science ; Sociology Machine Learning in Materials Science ; Injection Molding Process and Properties ; Manufacturing Process and Optimization
Authors: Poushali Das | Parham Khoshbakht Marvi | Sayan Ganguly | Xiaowu Tang | Bo Wang | Seshasai Srinivasan | Amin Reza Rajabzadeh | Andreas Rosenkranz
Source: Nano-Micro Letters (review, 2024-02-27)
Field: Materials Science — Materials Chemistry

MXene-Based Elastomer Mimetic Stretchable Sensors: Design, Properties, and Applications Flexible sensors based on MXene-polymer composites are highly prospective for next-generation wearable electronics used in human-machine interfaces. One of the motivating factors behind the progress of flexible sensors is the steady arrival of new conductive materials. MXenes, a new family of 2D nanomaterials, have been drawing attention since the last decade due to their high electronic conductivity, processability, mechanical robustness and chemical tunability. In this review, we encompass the fabrication of MXene-based polymeric nanocomposites, their structure-property relationship, and applications in the flexible sensor domain. Moreover, our discussion is not only limited to sensor design, their mechanism, and various modes of sensing platform, but also their future perspective and market throughout the world. With our article, we intend to fortify the bond between flexible matrices and MXenes thus promoting the swift advancement of flexible MXene-sensors for wearable technologies. MXenes ; Materials science ; Robustness (evolution) ; Wearable technology ; Wearable computer ; Nanotechnology ; Electronics ; Flexible electronics ; Fabrication ; Elastomer ; Stretchable electronics ; Microelectromechanical systems ; Electrical conductor ; Flexibility (engineering) ; Computer science ; Electrical engineering ; Engineering ; Embedded system ; Composite material MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Shuhao Zhang | Małgorzata Z. Makoś | Ryan B. Jadrich | Elfi Kraka | Kipton Barros | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev | Nicholas Lubbers | Richard A. Messerly | Justin S. Smith
Source: Nature Chemistry (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation. Chemistry ; Reactive intermediate ; Nanotechnology ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Paweł Grzybek | Gabriela Dudek | Bart Van der Bruggen
Source: Chemical Engineering Journal (review, 2024-06-29)
Field: Materials Science — Biomaterials

Cellulose-based films and membranes: A comprehensive review on preparation and applications Cellulose is the most abundant biopolymer in nature and its application for the preparation of numerous materials is of great interest. Among the various properties of cellulose materials described in the literature, their barrier and transport properties are among the most prominent ones. Therefore, this paper presents an overview of the barrier and transport properties of cellulose flat materials such as membranes and films as well as a description of their structure, preparation methods and application. The novel approach of the article is the statistical evaluation of the membrane efficiency based on the multiplication of the flux and retention factor to the efficiency of other biopolymer membranes. The first part of the work involves an overview of cellulose structure and physico-chemical properties. In the next section, the methods of preparation of cellulose materials with focus on the dissolution-regeneration method and bacterial synthesis are analysed. Furthermore, basic mechanisms of molecule transportation through polymeric materials, describing barrier and transport properties were introduced and assessed. The main part of the article regarding to barrier and transport properties of cellulose based membranes and films highlights gas permeation, vapours separation, dyes, ions and macromolecular compounds separation and two-phases mixtures separation. Cellulose ; Membrane ; Biopolymer ; Bacterial cellulose ; Permeation ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry ; Polymer ; Engineering Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Xuan Meng | Yongli Shen | Huanyu Zhao | Xinlei Lu | Zheng Wang | Yanjun Zhao
Source: Journal of Nanobiotechnology (review, 2024-09-28)
Field: Materials Science — Biomaterials

Redox-manipulating nanocarriers for anticancer drug delivery: a systematic review Spatiotemporally controlled cargo release is a key advantage of nanocarriers in anti-tumor therapy. Various external or internal stimuli-responsive nanomedicines have been reported for their ability to increase drug levels at the diseased site and enhance therapeutic efficacy through a triggered release mechanism. Redox-manipulating nanocarriers, by exploiting the redox imbalances in tumor tissues, can achieve precise drug release, enhancing therapeutic efficacy while minimizing damage to healthy cells. As a typical redox-sensitive bond, the disulfide bond is considered a promising tool for designing tumor-specific, stimulus-responsive drug delivery systems (DDS). The intracellular redox imbalance caused by tumor microenvironment (TME) regulation has emerged as an appealing therapeutic target for cancer treatment. Sustained glutathione (GSH) depletion in the TME by redox-manipulating nanocarriers can exacerbate oxidative stress through the exchange of disulfide-thiol bonds, thereby enhancing the efficacy of ROS-based cancer therapy. Intriguingly, GSH depletion is simultaneously associated with glutathione peroxidase 4 (GPX4) inhibition and dihydrolipoamide S-acetyltransferase (DLAT) oligomerization, triggering mechanisms such as ferroptosis and cuproptosis, which increase the sensitivity of tumor cells. Hence, in this review, we present a comprehensive summary of the advances in disulfide based redox-manipulating nanocarriers for anticancer drug delivery and provide an overview of some representative achievements for combinational therapy and theragnostic. The high concentration of GSH in the TME enables the engineering of redox-responsive nanocarriers for GSH-triggered on-demand drug delivery, which relies on the thiol-disulfide exchange reaction between GSH and disulfide-containing vehicles. Conversely, redox-manipulating nanocarriers can deplete GSH, thereby enhancing the efficacy of ROS-based treatment nanoplatforms. In brief, we summarize the up-to-date developments of the redox-manipulating nanocarriers for cancer therapy based on DDS and provide viewpoints for the establishment of more stringent anti-tumor nanoplatform. Nanocarriers ; Chemistry ; Anticancer drug ; Pharmacology ; Drug delivery ; Nanotechnology ; Drug ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Ibtesam Waheed | Anwar Ali | Huma Tabassum | Narjis Khatoon | Wing‐Fu Lai | Xin Zhou
Source: Frontiers in Oncology (article, 2024-04-10)
Field: Materials Science — Biomaterials

Lipid-based nanoparticles as drug delivery carriers for cancer therapy Cancer is a severe disease that results in death in all countries of the world. A nano-based drug delivery approach is the best alternative, directly targeting cancer tumor cells with improved drug cellular uptake. Different types of nanoparticle-based drug carriers are advanced for the treatment of cancer, and to increase the therapeutic effectiveness and safety of cancer therapy, many substances have been looked into as drug carriers. Lipid-based nanoparticles (LBNPs) have significantly attracted interest recently. These natural biomolecules that alternate to other polymers are frequently recycled in medicine due to their amphipathic properties. Lipid nanoparticles typically provide a variety of benefits, including biocompatibility and biodegradability. This review covers different classes of LBNPs, including their characterization and different synthesis technologies. This review discusses the most significant advancements in lipid nanoparticle technology and their use in medicine administration. Moreover, the review also emphasized the applications of lipid nanoparticles that are used in different cancer treatment types. Drug delivery ; Biocompatibility ; Cancer ; Drug ; Drug carrier ; Nanotechnology ; Cancer therapy ; Nanomedicine ; Solid lipid nanoparticle ; Nanoparticle ; Medicine ; Pharmacology ; Chemistry ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Lipid Membrane Structure and Behavior
Authors: Bin Lu | Yuze Xia | Yuqian Ren | Xie Miaomiao | Liguo Zhou | Giovanni Vinai | Simon A. Morton | Andrew T. S. Wee | Wilfred G. van der Wiel | Wen Zhang | Ping Kwan Johnny Wong
Source: Advanced Science (review, 2024-01-26)
Field: Materials Science — Materials Chemistry

When Machine Learning Meets 2D Materials: A Review The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area. Computer science ; Stacking ; Artificial intelligence ; Resource (disambiguation) ; Layer (electronics) ; Data science ; Machine learning ; Nanotechnology ; Materials science ; Physics 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Mohammadtaghi Vakili | Petr Koutnı́k | Jan Kohout
Source: Sustainability (article, 2024-02-17)
Field: Materials Science — Materials Chemistry

Addressing Hydrogen Sulfide Corrosion in Oil and Gas Industries: A Sustainable Perspective In the oil and gas industry, the corrosion attributed to hydrogen sulfide (H2S) is one of the most significant challenges. This review paper systematically investigates the diverse facets of H2S corrosion, including its sources, corrosion locations, mechanisms, and resultant corrosion products. Understanding different forms of H2S corrosion, such as stress-oriented hydrogen-induced cracking (SO-HIC), sulfide stress cracking (SSC), and hydrogen-induced cracking (HIC), provides a thorough comprehension of these phenomena. The paper discusses critical factors influencing H2S corrosion, such as temperature, flow rate, pH, and H2S concentration, highlighting their implications for sustainable practices in the oil and gas sector. The review emphasizes the significance of monitoring and mitigation strategies, covering continuous monitoring, applying corrosion inhibitors, selecting materials, and conducting thorough data analysis and reporting. Furthermore, the role of training in fostering a sustainable approach to H2S corrosion management is highlighted. This exploration advances the overarching goal of sustainable development in the oil and gas industries by providing insights into understanding, monitoring, and mitigating H2S corrosion. The findings presented here offer a foundation for developing environmentally conscious strategies and practices to guarantee the long-term viability and flexibility of refinery operations. Corrosion ; Hydrogen sulfide ; Sour gas ; Corrosion monitoring ; Stress corrosion cracking ; Flexibility (engineering) ; Refinery ; Fossil fuel ; Integrity management ; Environmental science ; Petroleum industry ; Cracking ; Materials science ; Waste management ; Petroleum engineering ; Engineering ; Metallurgy ; Environmental engineering ; Sulfur ; Natural gas ; Pipeline transport ; Management ; Economics Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Jiantang Li | Banglin Chen
Source: Chemical Science (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Flexible hydrogen-bonded organic frameworks (HOFs): opportunities and challenges Flexible behavior is one of the most fascinating features of hydrogen-bonded organic frameworks (HOFs), which represent an emerging class of porous materials that are self-assembled <i>via</i> H-bonding between organic building units. Due to their unique flexibility, HOFs can undergo structural changes or transformations in response to various stimuli (physical or chemical). Taking advantage of this unique structural feature, flexible HOFs show potential in multifunctional applications such as gas storage/separation, molecular recognition, sensing, proton conductivity, biomedicine, <i>etc.</i> While some other flexible porous materials have been extensively studied, the dynamic behavior of HOFs remains relatively less explored. This perspective highlights the inherent flexible properties of HOFs, discusses their different flexible behaviors, including pore size/shape changes, interpenetration/stacking manner, H-bond breaking/reconstruction, and local dynamic behavior, and highlights their potential applications. We believe that this perspective will not only contribute to HOF chemistry and materials science, but will also facilitate the ongoing extensive research on dynamic porous materials. Business Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Chemical Synthesis and Reactions
Authors: Hua Zhang | K. Lu | Malik Ebbini | Penghsuan Huang | Haiyan Lu | Lingjun Li
Source: npj Imaging (review, 2024-07-17)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging for spatially resolved multi-omics molecular mapping The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future. Lipidomics ; Metabolomics ; Computational biology ; Proteomics ; Proteome ; Omics ; Lipidome ; Mass spectrometry imaging ; Metabolome ; Data science ; Computer science ; Biology ; Mass spectrometry ; Bioinformatics ; Chemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ronghui Lou | Wenqing Shui
Source: Molecular & Cellular Proteomics (article, 2024-01-03)
Field: Chemistry — Spectroscopy

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023 Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics. Computer science ; Workflow ; Proteomics ; Software ; Data acquisition ; Data mining ; Data science ; Database ; Biology ; Operating system Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Anand Raj Dhanapal | Muthu Thiruvengadam | Jayavarshini Vairavanathan | Baskar Venkidasamy | Maheswaran Easwaran | Mansour Ghorbanpour
Source: ACS Omega (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Nanotechnology Approaches for the Remediation of Agricultural Polluted Soils Soil pollution from various anthropogenic and natural activities poses a significant threat to the environment and human health. This study explored the sources and types of soil pollution and emphasized the need for innovative remediation approaches. Nanotechnology, including the use of nanoparticles, is a promising approach for remediation. Diverse types of nanomaterials, including nanobiosorbents and nanobiosurfactants, have shown great potential in soil remediation processes. Nanotechnology approaches to soil pollution remediation are multifaceted. Reduction reactions and immobilization techniques demonstrate the versatility of nanomaterials in mitigating soil pollution. Nanomicrobial-based bioremediation further enhances the efficiency of pollutant degradation in agricultural soils. A literature-based screening was conducted using different search engines, including PubMed, Web of Science, and Google Scholar, from 2010 to 2023. Keywords such as "soil pollution, nanotechnology, nanoremediation, heavy metal remediation, soil remediation" and combinations of these were used. The remediation of heavy metals using nanotechnology has demonstrated promising results and offers an eco-friendly and sustainable solution to address this critical issue. Nanobioremediation is a robust strategy for combatting organic contamination in soils, including pesticides and herbicides. The use of nanophytoremediation, in which nanomaterials assist plants in extracting and detoxifying pollutants, represents a cutting-edge and environmentally friendly approach for tackling soil pollution. Environmental remediation ; Environmental science ; Pollutant ; Bioremediation ; Pollution ; Soil contamination ; Soil water ; Environmental chemistry ; Contamination ; Chemistry ; Soil science ; Ecology Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Graphene and Nanomaterials Applications
Authors: Maksim Kulichenko | Benjamin Nebgen | Nicholas Lubbers | Justin S. Smith | Kipton Barros | Alice E. A. Allen | Adela Habib | Emily Shinkle | Nikita Fedik | Ying Wai Li | Richard A. Messerly | Sergei Tretiak
Source: Chemical Reviews (review, 2024-11-21)
Field: Materials Science — Materials Chemistry

Data Generation for Machine Learning Interatomic Potentials and Beyond The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space. Chemical space ; Transferability ; Reliability (semiconductor) ; Field (mathematics) ; Space (punctuation) ; Computer science ; Data acquisition ; Data science ; Cover (algebra) ; Machine learning ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications