jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nam Heon Cho | Andrés Guerrero‐Martínez | Jessica Ma | Sara Bals | Nicholas A. Kotov | Luis M. Liz‐Marzán | Ki Tae Nam
Source: Nature Reviews Bioengineering (article, 2023-01-27)
Field: Materials Science — Biomaterials

Bioinspired chiral inorganic nanomaterials From small molecules to entire organisms, evolution has refined biological structures at the nanoscale, microscale and macroscale to be chiral—that is, mirror dissymmetric. Chirality results in biological, chemical and physical properties that can be influenced by circularly polarized electromagnetic fields. Chiral nanoscale materials can be designed that mimic, refine and advance biological chiral geometries, to engineer optical, physical and chemical properties for applications in photonics, sensing, catalysis and biomedicine. In this Review, we discuss the mechanisms underlying chirality transfer in nature and provide design principles for chiral nanomaterials. We highlight how chiral features emerge in inorganic materials during the chemical synthesis of chiral nanostructures, and outline key applications for inorganic chiral nanomaterials, including promising designs for biomedical applications, such as biosensing and immunomodulation. We conclude with an outlook to future opportunities and challenges, including the need for refined characterization techniques. Chiral inorganic nanomaterials can induce specific physical, chemical and biological phenomena. This Review discusses how chiral biomolecules and polarized light allow nanoscale chirality control in inorganic nanomaterials, which can be applied in optical devices, sensing, catalysis and biomedicine. Nanomaterials ; Nanotechnology ; Biomolecule ; Chirality (physics) ; Microscale chemistry ; Materials science ; Nanoscopic scale ; Biosensor ; Nanobiotechnology ; Nanostructure ; Characterization (materials science) ; Physics ; Nanoparticle Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Luojun Du | Maciej R. Molas | Zhiheng Huang | Guangyu Zhang | Feng Wang | Zhipei Sun
Source: Science (review, 2023-03-31)
Field: Materials Science — Materials Chemistry

Moiré photonics and optoelectronics Moiré superlattices, the artificial quantum materials, have provided a wide range of possibilities for the exploration of completely new physics and device architectures. In this Review, we focus on the recent progress on emerging moiré photonics and optoelectronics, including but not limited to moiré excitons, trions, and polaritons; resonantly hybridized excitons; reconstructed collective excitations; strong mid- and far-infrared photoresponses; terahertz single-photon detection; and symmetry-breaking optoelectronics. We also discuss the future opportunities and research directions in this field, such as developing advanced techniques to probe the emergent photonics and optoelectronics in an individual moiré supercell; exploring new ferroelectric, magnetic, and multiferroic moiré systems; and using external degrees of freedom to engineer moiré properties for exciting physics and potential technological innovations. Photonics ; Superlattice ; Optoelectronics ; Exciton ; Terahertz radiation ; Polariton ; Physics ; Photon ; Materials science ; Nanotechnology ; Optics ; Condensed matter physics 2D Materials and Applications ; Perovskite Materials and Applications ; Nanowire Synthesis and Applications
Authors: James A. Butler | Richard D. Handy | Mathew Upton | Alexandros Besinis
Source: ACS Nano (review, 2023-04-07)
Field: Materials Science — Materials Chemistry

Review of Antimicrobial Nanocoatings in Medicine and Dentistry: Mechanisms of Action, Biocompatibility Performance, Safety, and Benefits Compared to Antibiotics This review discusses topics relevant to the development of antimicrobial nanocoatings and nanoscale surface modifications for medical and dental applications. Nanomaterials have unique properties compared to their micro- and macro-scale counterparts and can be used to reduce or inhibit bacterial growth, surface colonization and biofilm development. Generally, nanocoatings exert their antimicrobial effects through biochemical reactions, production of reactive oxygen species or ionic release, while modified nanotopographies create a physically hostile surface for bacteria, killing cells via biomechanical damage. Nanocoatings may consist of metal nanoparticles including silver, copper, gold, zinc, titanium, and aluminum, while nonmetallic compounds used in nanocoatings may be carbon-based in the form of graphene or carbon nanotubes, or composed of silica or chitosan. Surface nanotopography can be modified by the inclusion of nanoprotrusions or black silicon. Two or more nanomaterials can be combined to form nanocomposites with distinct chemical or physical characteristics, allowing combination of different properties such as antimicrobial activity, biocompatibility, strength, and durability. Despite their wide range of applications in medical engineering, questions have been raised regarding potential toxicity and hazards. Current legal frameworks do not effectively regulate antimicrobial nanocoatings in matters of safety, with open questions remaining about risk analysis and occupational exposure limits not considering coating-based approaches. Bacterial resistance to nanomaterials is also a concern, especially where it may affect wider antimicrobial resistance. Nanocoatings have excellent potential for future use, but safe development of antimicrobials requires careful consideration of the "One Health" agenda, appropriate legislation, and risk assessment. Nanotechnology ; Materials science ; Nanomaterials ; Biocompatibility ; Nanotopography ; Antimicrobial ; Chemistry ; Metallurgy Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Bone Tissue Engineering Materials
Authors: Dimitrios Angelis | Filippos Sofos | Theodoros E. Karakasidis
Source: Archives of Computational Methods in Engineering (review, 2023-04-19)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives The online version contains supplementary material available at 10.1007/s11831-023-09922-z. Symbolic regression ; Genetic programming ; Computer science ; Regression ; Artificial intelligence ; Regression analysis ; Data science ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Evolutionary Algorithms and Applications ; Computational Drug Discovery Methods
Authors: Vitthal B. Saptal | Vincenzo Ruta | Mark A. Bajada | Gianvito Vilé
Source: Angewandte Chemie International Edition (review, 2023-03-15)
Field: Chemistry — Inorganic Chemistry

Single‐Atom Catalysis in Organic Synthesis Single-atom catalysts hold the potential to significantly impact the chemical sector, pushing the boundaries of catalysis in new, uncharted directions. These materials, featuring isolated metal species ligated on solid supports, can exist in many coordination environments, all of which have shown important functions in specific transformations. Their emergence has also provided exciting opportunities for mimicking metalloenzymes and bridging the gap between homogeneous and heterogeneous catalysis. This Review outlines the impressive progress made in recent years regarding the use of single-atom catalysts in organic synthesis. We also illustrate potential knowledge gaps in the search for more sustainable, earth-abundant single-atom catalysts for synthetic applications. Catalysis ; Homogeneous ; Atom (system on chip) ; Bridging (networking) ; Nanotechnology ; Organic synthesis ; Homogeneous catalysis ; Chemistry ; Combinatorial chemistry ; Materials science ; Biochemical engineering ; Computer science ; Organic chemistry ; Physics ; Engineering Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Alemayehu Godana Birhanu
Source: Clinical Proteomics (review, 2023-08-26)
Field: Chemistry — Spectroscopy

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories. Proteomics ; Biomarker discovery ; Turnaround time ; Biomarker ; Data science ; Medicine ; Bioinformatics ; Computational biology ; Computer science ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Thomas Vangijzegem | Valentin Lecomte | Indiana Ternad | Levy Van Leuven | Robert N. Müller | Dimitri Stanicki | Sophie Laurent
Source: Pharmaceutics (review, 2023-01-10)
Field: Materials Science — Biomaterials

Superparamagnetic Iron Oxide Nanoparticles (SPION): From Fundamentals to State-of-the-Art Innovative Applications for Cancer Therapy Despite significant advances in cancer therapy over the years, its complex pathological process still represents a major health challenge when seeking effective treatment and improved healthcare. With the advent of nanotechnologies, nanomedicine-based cancer therapy has been widely explored as a promising technology able to handle the requirements of the clinical sector. Superparamagnetic iron oxide nanoparticles (SPION) have been at the forefront of nanotechnology development since the mid-1990s, thanks to their former role as contrast agents for magnetic resonance imaging. Though their use as MRI probes has been discontinued due to an unfavorable cost/benefit ratio, several innovative applications as therapeutic tools have prompted a renewal of interest. The unique characteristics of SPION, i.e., their magnetic properties enabling specific response when submitted to high frequency (magnetic hyperthermia) or low frequency (magneto-mechanical therapy) alternating magnetic field, and their ability to generate reactive oxygen species (either intrinsically or when activated using various stimuli), make them particularly adapted for cancer therapy. This review provides a comprehensive description of the fundamental aspects of SPION formulation and highlights various recent approaches regarding in vivo applications in the field of cancer therapy. Nanomedicine ; Cancer therapy ; Nanotechnology ; Cancer treatment ; Cancer ; Materials science ; Medicine ; Computer science ; Nanoparticle ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Fasikaw Kibrete | Tomasz Trzepieciński | Hailu Shimels Gebremedhen | Dereje Engida Woldemichael
Source: Journal of Composites Science (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain. Artificial intelligence ; Field (mathematics) ; Strengths and weaknesses ; Process (computing) ; Computer science ; Machine learning ; Domain (mathematical analysis) ; Mathematics ; Psychology Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Mineral Processing and Grinding
Authors: Pavel Sinitcyn | Alicia Richards | Robert J. Weatheritt | Dain R. Brademan | Harald Marx | Evgenia Shishkova | Jesse G. Meyer | Alexander S. Hebert | Michael S. Westphall | Benjamin J. Blencowe | Jürgen Cox | Joshua J. Coon
Source: Nature Biotechnology (article, 2023-03-23)
Field: Chemistry — Spectroscopy

Global detection of human variants and isoforms by deep proteome sequencing An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon-exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated. Protein isoform ; Biology ; Proteome ; Exon ; Computational biology ; Human proteome project ; Shotgun proteomics ; Proteomics ; Alternative splicing ; Gene isoform ; Deep sequencing ; Genetics ; Protein sequencing ; Peptide sequence ; Gene ; Genome Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Molecular Biology Techniques and Applications
Authors: Dylan M. Anstine | Olexandr Isayev
Source: The Journal of Physical Chemistry A (review, 2023-02-21)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials and Long-Range Physics Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of this, we present a Perspective focused on key methodologies and models being used where the presence of nonlocal physics and chemistry are crucial for describing system properties. The strategies covered include MLIPs augmented with dispersion corrections, electrostatics calculated with charges predicted from atomic environment descriptors, the use of self-consistency and message passing iterations to propagated nonlocal system information, and charges obtained via equilibration schemes. We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only nearsighted terms are deficient. Electrostatics ; Range (aeronautics) ; Consistency (knowledge bases) ; Statistical physics ; Ab initio ; Computer science ; Dispersion (optics) ; Physics ; Physical system ; Atom (system on chip) ; Artificial intelligence ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hakan Demir | Hilal Daglar | Hasan Can Gülbalkan | Gokhan Onder Aksu | Seda Keskın
Source: Coordination Chemistry Reviews (article, 2023-03-21)
Field: Chemistry — Inorganic Chemistry

Recent advances in computational modeling of MOFs: From molecular simulations to machine learning The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation of an almost boundless number of materials some of which can be a substitute for the traditionally used porous materials in various fields including gas storage and separation, catalysis, drug storage and delivery. The number of MOFs and their potential applications are growing so quickly that, when novel MOFs are synthesized, testing them for all possible applications is not practical. High-throughput computational screening approaches based on molecular simulations of materials have been widely used to investigate MOFs and identify the optimal MOFs for a specific application. Despite the growing computational resources, given the enormous MOF material space, computational identification of promising MOFs requires more efficient approaches in terms of time and effort. Leveraging data-driven science techniques can offer key benefits such as accelerated MOF design and discovery pathways via the establishment of machine learning (ML) models and interpretation of complex structure-performance relationships that can reach beyond expert intuition. In this review, we present key scientific breakthroughs that propelled computational modeling of MOFs and discuss the state-of-the-art approaches extending from molecular simulations to ML algorithms. Finally, we provide our perspective on the potential opportunities and challenges for the future of big data-driven MOF design and discovery. Computational model ; Biochemical engineering ; Nanotechnology ; Computer science ; Chemical space ; Intuition ; Chemistry ; Data science ; Drug discovery ; Artificial intelligence ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Mingyue Liu | Yuyuan Ye | Jiamin Ye | Ting Gao | Dehua Wang | Gang Chen | Zhenjun Song
Source: Magnetochemistry (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Recent Advances of Magnetite (Fe3O4)-Based Magnetic Materials in Catalytic Applications Catalysts play a critical role in producing most industrial chemicals and are essential to environmental remediation. Under the demands of sustainable development, environment protection, and cost-related factors, it has been suggested that catalysts are sufficiently separable and conveniently recyclable in the catalysis process. Magnetite (Fe3O4) nanomaterials provide a possible way to achieve this goal, due to their magnetism, chemical stability, low toxicity, economic viability, etc. Therefore, Fe3O4-based materials are emerging as an important solid support to load heterogeneous catalysts and immobilize homogeneous catalysts. Moreover, the addition of magnetic character to catalysts will not only make their recovery much easier but also possibly endow catalysts with desirable properties, such as magnetothermal conversion, Lewis acid, mimetic enzyme activity, and Fenton activity. The following review comprises a short survey of the most recent reports in the catalytic applications of Fe3O4-based magnetic materials. It contains seven sections, an introduction into the theme, applications of Fe3O4-based magnetic materials in environmental remediation, electrocatalysis, organic synthesis, catalytic synthesis of biodiesel, and cancer treatment, and conclusions about the reported research with perspectives for future developments. Elucidation of the functions and mechanisms of Fe3O4 nanoparticles (NPs) in these applications may benefit the acquisition of robust and affordable protocols, leading to catalysts with good catalytic activity and enhanced recoverability. Catalysis ; Magnetite ; Environmental remediation ; Nanomaterials ; Nanotechnology ; Materials science ; Magnetism ; Biochemical engineering ; Chemistry ; Organic chemistry ; Engineering Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanomaterials for catalytic reactions
Authors: Mahbuba Rahman
Source: Nanotheranostics (review, 2023-01-01)
Field: Materials Science — Biomaterials

Magnetic Resonance Imaging and Iron-oxide Nanoparticles in the era of Personalized Medicine Medical imaging is an important factor for diagnosis. It can be used to diagnose patients, differentiate disease stages, and monitor treatment regimens. Although different imaging technologies are available, MRI is sensitive over other imaging modalities as it is capable of deep tissue penetration allowing to image the anatomical, structural, and molecular level of diseased organs. Thus, it can be used as screening tool for disease staging. One of the important components of imaging is contrast agents which are used to increase the sensitivity of MRI technology. While different types of contrast agents are available, iron-oxide based nanoparticles (IONPS) are widely used as these are easy to formulate, functionalize, biocompatible and cost effective. In addition to its use as contrast agents, these have been used as drug carriers for the treatment of different types of diseases ranging from cancer, cardiovascular diseases, neurological disorders, autoimmune diseases, and infectious diseases. For the last two decades, there has been advancement in nanotheranostics, where IONPs are formulated to carry drug and be used as contrast agents in one system so that these can be used for image-guided therapy and monitor real-life treatment response in diseased tissue. This technology can be used to stratify patients into responders and non-responders and reduce adverse drug toxicity and lead to a tailored treatment. However, success of nanotheranostics depends on several factor, including identification of disease associated biomarkers that can be targeted on IONPs during formulation. While many challenges exist for the clinical translation of nanotheranostics, it still has the potential to be implemented in personalized treatment strategy. In this review article, we discussed the use of MRI technology and IONPs in relation to their application in disease diagnosis and nanotheranostics application in personalized medicine. Medicine ; Magnetic resonance imaging ; Personalized medicine ; Iron oxide nanoparticles ; Molecular imaging ; Precision medicine ; Disease ; Modalities ; Drug ; Medical imaging ; Pathology ; Bioinformatics ; Radiology ; Nanotechnology ; Pharmacology ; Nanoparticle ; Materials science ; In vivo Nanoparticle-Based Drug Delivery ; Ultrasound and Hyperthermia Applications ; MRI in cancer diagnosis