jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mike Hardy | Pola Goldberg Oppenheimer
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

‘When is a hotspot a good nanospot’ – review of analytical and hotspot-dominated surface enhanced Raman spectroscopy nanoplatforms Substrate development in surface-enhanced Raman spectroscopy (SERS) continues to attract research interest. In order to determine performance metrics, researchers in foundational SERS studies use a variety of experimental means to characterize the nature of substrates. However, often this process would appear to be performed indiscriminately without consideration for the physical scale of the enhancement phenomena. Herein, we differentiate between SERS substrates whose primary enhancing structures are on the hundreds of nanometer scale (analytical SERS nanosubstrates) and those whose main mechanism derives from nanometric-sized gaps (hot-spot dominated SERS substrates), assessing the utility of various characterization methods for each substrate class. In this context, characterization approaches in white-light spectroscopy, electron beam methods, and scanning probe spectroscopies are reviewed. Tip-enhanced Raman spectroscopy, wavelength-scanned SERS studies, and the impact of surface hydrophobicity are also discussed. Conclusions are thus drawn on the applicability of each characterization technique regarding amenability for SERS experiments that have features at different length scales. For instance, while white light spectroscopy can provide an indication of the plasmon resonances associated with 10 s-100 s nm-scale structures, it may not reveal information about finer surface texturing on the true nm-scale, critical for SERS' sensitivity, and in need of investigation <i>via</i> scanning probe techniques. Hotspot (geology) ; Raman spectroscopy ; Materials science ; Surface-enhanced Raman spectroscopy ; Nanotechnology ; Optics ; Geology ; Raman scattering ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Quantum Dots Synthesis And Properties
Authors: Shaquib Rahman Ansari | Yael del Carmen Suárez‐López | Thomas Thersleff | Lennart Häggström | Tore Ericsson | Ioannis Katsaros | Michelle Åhlén | Maria Karlgren | Peter Svedlindh | Carlos Rinaldi | Alexandra Teleki
Source: ACS Nano (article, 2024-05-30)
Field: Materials Science — Biomaterials

Pharmaceutical Quality by Design Approach to Develop High-Performance Nanoparticles for Magnetic Hyperthermia Magnetic hyperthermia holds significant therapeutic potential, yet its clinical adoption faces challenges. One obstacle is the large-scale synthesis of high-quality superparamagnetic iron oxide nanoparticles (SPIONs) required for inducing hyperthermia. Robust and scalable manufacturing would ensure control over the key quality attributes of SPIONs, and facilitate clinical translation and regulatory approval. Therefore, we implemented a risk-based pharmaceutical quality by design (QbD) approach for SPION production using flame spray pyrolysis (FSP), a scalable technique with excellent batch-to-batch consistency. A design of experiments method enabled precise size control during manufacturing. Subsequent modeling linked the SPION size (6-30 nm) and composition to intrinsic loss power (ILP), a measure of hyperthermia performance. FSP successfully fine-tuned the SPION composition with dopants (Zn, Mn, Mg), at various concentrations. Hyperthermia performance showed a strong nonlinear relationship with SPION size and composition. Moreover, the ILP demonstrated a stronger correlation to coercivity and remanence than to the saturation magnetization of SPIONs. The optimal operating space identified the midsized (15-18 nm) Mn<sub>0.25</sub>Fe<sub>2.75</sub>O<sub>4</sub> as the most promising nanoparticle for hyperthermia. The production of these nanoparticles on a pilot scale showed the feasibility of large-scale manufacturing, and cytotoxicity investigations in multiple cell lines confirmed their biocompatibility. <i>In vitr</i>o hyperthermia studies with Caco-2 cells revealed that Mn<sub>0.25</sub>Fe<sub>2.75</sub>O<sub>4</sub> nanoparticles induced 80% greater cell death than undoped SPIONs. The systematic QbD approach developed here incorporates process robustness, scalability, and predictability, thus, supporting the clinical translation of high-performance SPIONs for magnetic hyperthermia. Magnetic hyperthermia ; Materials science ; Magnetic nanoparticles ; Quality by Design ; Hyperthermia ; Nanotechnology ; Nanoparticle ; Biomedical engineering ; Particle size ; Chemical engineering ; Medicine Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Iron oxide chemistry and applications
Authors: Ryma Merdoud | Farid Aoudjit | Lotfi Mouni | Vivek V. Ranade
Source: Ultrasonics Sonochemistry (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

Degradation of methyl orange using hydrodynamic Cavitation, H2O2, and photo-catalysis with TiO2-Coated glass Fibers: Key operating parameters and synergistic effects Advanced oxidation processes (AOPs) are eco-friendly, and promising technology for treating dye containing wastewater. This study focuses on investigating the removal of methyl orange (MO), an azo dye, from a synthetic wastewater through the use of hydrodynamic cavitation (HC), both independently and in combination with hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), as an external oxidant, as well as photocatalysis (PC) employing catalyst coated on glass fibers tissue (GFT). The examination of various operating parameters, including the pressure drop and the concentration of H<sub>2</sub>O<sub>2</sub>, was systematically conducted to optimize the degradation of MO. A per-pass degradation modelwas used to interpret and describe the experimental data. The data revealed that exclusive employment of HC using a vortex-based cavitation device at 1.5 bar pressure drop, resulted in a degradation exceeding 96 % after 100 passes, equivalent to 230 min of treatment (cavitation yield of 3.6 mg/kJ for HC), with a COD mineralization surpassing 12 %. The presence of a small amount of H<sub>2</sub>O<sub>2</sub> (0.01 %) significantly reduced the degradation time from 230 min to 36 min (16 passes), achieving a degradation of 99.8 % (cavitation yield of 6.77 mg/kJ for HC) with COD mineralization rate twice as much as HC alone, indicating a synergistic effect of 4.8. The degradation time was further reduced to 21 min by combining HC with PC using TiO<sub>2</sub>-coated glass fibers and H<sub>2</sub>O<sub>2</sub>, (cavitation yield of 11.83 mg/kJ for HC), resulting in an impressive synergistic effect of 9.2 and COD mineralization twice as high as the HC/H<sub>2</sub>O<sub>2</sub> system. The results demonstrate that HC based hybrid AOPs can be very effective for treating and mineralizing azo dyes in water. Cavitation ; Hydrogen peroxide ; Catalysis ; Mineralization (soil science) ; Photocatalysis ; Methyl orange ; Degradation (telecommunications) ; Chemistry ; Wastewater ; Chemical engineering ; Drop (telecommunication) ; Yield (engineering) ; Nuclear chemistry ; Materials science ; Composite material ; Environmental engineering ; Organic chemistry ; Nitrogen Ultrasound and Cavitation Phenomena
Authors: Athanasios Litsios | Benjamin Grys | Oren Kraus | Helena Friesen | Catherine Ross | Myra Paz David Masinas | Duncan T. Forster | Mary Couvillion | Stefanie Timmermann | Maximilian Billmann | Chad L. Myers | Nils Johnsson | L. Stirling Churchman | Charles Boone | Brenda Andrews
Source: Cell (article, 2024-03-01)
Field: Chemistry — Spectroscopy

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level. Proteome ; Biology ; Cell cycle ; Protein subcellular localization prediction ; Cell biology ; Subcellular localization ; Compartment (ship) ; Cell Cycle Protein ; Cell ; Computational biology ; Proteomics ; Bioinformatics ; Cytoplasm ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Bioinformatics and Genomic Networks
Authors: Daniela Klaproth-Andrade | Johannes Hingerl | Yanik Bruns | Nicholas H. Smith | Jakob Träuble | Mathias Wilhelm | Julien Gagneur
Source: Nature Communications (article, 2024-01-02)
Field: Chemistry — Spectroscopy

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing. Computational biology ; DNA sequencing ; Proteogenomics ; Tandem mass spectrometry ; Computer science ; Identification (biology) ; Biology ; Mass spectrometry ; Chemistry ; Genetics ; Genomics ; Genome ; DNA ; Gene ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Priyadarshini Mohapatra | Mohanraj Gopikrishnan | C. George Priya Doss | Natarajan Chandrasekaran
Source: International Journal of Nanomedicine (review, 2024-03-01)
Field: Materials Science — Biomaterials

How Precise are Nanomedicines in Overcoming the Blood–Brain Barrier? A Comprehensive Review of the Literature New nanotechnology strategies for enhancing drug delivery in brain disorders have recently received increasing attention from drug designers. The treatment of neurological conditions, including brain tumors, stroke, Parkinson's Disease (PD), and Alzheimer's disease (AD), may be greatly influenced by nanotechnology. Numerous studies on neurodegeneration have demonstrated the effective application of nanomaterials in the treatment of brain illnesses. Nanocarriers (NCs) have made it easier to deliver drugs precisely to where they are needed. Thus, the most effective use of nanomaterials is in the treatment of various brain diseases, as this amplifies the overall impact of medication and emphasizes the significance of nanotherapeutics through gene therapy, enzyme replacement therapy, and blood-barrier mechanisms. Recent advances in nanotechnology have led to the development of multifunctional nanotherapeutic agents, a promising treatment for brain disorders. This novel method reduces the side effects and improves treatment outcomes. This review critically assesses efficient nano-based systems in light of obstacles and outstanding achievements. Nanocarriers that transfer medications across the blood-brain barrier and nano-assisted therapies, including nano-immunotherapy, nano-gene therapy, nano enzyme replacement therapy, scaffolds, and 3D to 6D printing, have been widely explored for the treatment of brain disorders. This study aimed to evaluate existing literature regarding the use of nanotechnology in the development of drug delivery systems that can penetrate the blood-brain barrier (BBB) and deliver therapeutic agents to treat various brain disorders. Nanocarriers ; Medicine ; Drug delivery ; Blood–brain barrier ; Disease ; Applications of nanotechnology ; Drug ; Nanotechnology ; Pharmacology ; Central nervous system ; Pathology ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Mengyao Wu | Huihui Tao | Tiantian Xu | Xuejia Zheng | Chunmei Wen | Guoying Wang | Yali Peng | Yong Dai
Source: Proteome Science (review, 2024-09-20)
Field: Chemistry — Spectroscopy

Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research. Proteomics ; Data science ; Computational biology ; Computer science ; Geography ; Biology Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Advanced Biosensing Techniques and Applications
Authors: Andrew Rosen | Max C. Gallant | Janine George | Janosh Riebesell | Hrushikesh Sahasrabuddhe | Jimmy‐Xuan Shen | Mingjian Wen | Matthew L. Evans | Guido Petretto | David Waroquiers | Gian‐Marco Rignanese | Kristin A. Persson | Anubhav Jain | Alex M. Ganose
Source: The Journal of Open Source Software (article, 2024-01-07)
Field: Materials Science — Materials Chemistry

Jobflow: Computational Workflows Made Simple Bug Fixes 🐛 Fix autoreplace by @gpetretto in https://github.com/materialsproject/jobflow/pull/848 Full Changelog: https://github.com/materialsproject/jobflow/compare/v0.3.0...v0.3.1 Workflow ; Python (programming language) ; Adapter (computing) ; Workflow technology ; Simple (philosophy) ; Workflow engine ; Graph Machine Learning in Materials Science ; Scientific Computing and Data Management ; Reservoir Engineering and Simulation Methods
Authors: Joseph Holbrook | Gabrielle E. Kemper | Amanda B. Hummon
Source: Chemical Communications (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Quantitative mass spectrometry imaging: therapeutics &amp; biomolecules Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology. Biomolecule ; Mass spectrometry imaging ; Mass spectrometry ; Chemistry ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Sebastian Kokott | F. Merz | Yi Yao | Christian Carbogno | Mariana Rossi | Ville Havu | Markus Rampp | Matthias Scheffler | Volker Blüm
Source: The Journal of Chemical Physics (preprint, 2024-07-11)
Field: Materials Science — Surfaces, Coatings and Films

Efficient all-electron hybrid density functionals for atomistic simulations beyond 10 000 atoms Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance central processing unit (CPU) compute clusters. The introduction of several new refined message passing interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. In addition, we also compare the runtime performance of the PBE, HSE06, and PBE0 functionals. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA-based simulations for a broad range of chemical systems, including hybrid organic-inorganic perovskites, organic crystals, and ice crystals with up to 30 576 atoms (101 920 electrons described by 244 608 basis functions). Electron ; Statistical physics ; Electron density ; Physics ; Atomic physics ; Materials science ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Zhiqiang Niu | Wanhui Zhao | Hao Deng | Tian Lu | Valerie J. Pinfield | Pingwen Ming | Yun Wang
Source: ACS Nano (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures Multiscale design of catalyst layers (CLs) is important to advancing hydrogen electrochemical conversion devices toward commercialized deployment, which has nevertheless been greatly hampered by the complex interplay among multiscale CL components, high synthesis cost and vast design space. We lack rational design and optimization techniques that can accurately reflect the nanostructure-performance relationship and cost-effectively search the design space. Here, we fill this gap with a deep generative artificial intelligence (AI) framework, GLIDER, that integrates recent generative AI, data-driven surrogate techniques and collective intelligence to efficiently search the optimal CL nanostructures driven by their electrochemical performance. GLIDER achieves realistic multiscale CL digital generation by leveraging the dimensionality-reduction ability of quantized vector-variational autoencoder. The powerful generative capability of GLIDER allows the efficient search of the optimal design parameters for the Pt-carbon-ionomer nanostructures of CLs. We also demonstrate that GLIDER is transferable to other fuel cell electrode microstructure generation, <i>e.g</i>., fibrous gas diffusion layers and solid oxide fuel cell anode. GLIDER is of potential as a digital tool for the design and optimization of broad electrochemical energy devices. Materials science ; Fuel cells ; Scale (ratio) ; Nanostructure ; Catalysis ; Nanotechnology ; Layer (electronics) ; Hydrogen ; Computer science ; Chemical engineering ; Engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Memory and Neural Computing
Authors: Ruiming Zhu | Wei Nong | Shuya Yamazaki | Kedar Hippalgaonkar
Source: Matter (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

WyCryst: Wyckoff inorganic crystal generator framework Generator (circuit theory) ; Crystal (programming language) ; Materials science ; Crystallography ; Chemistry ; Computer science ; Physics ; Programming language ; Power (physics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Jason M. Szafron | Elbert Heng | Jack H. Boyd | Jay D. Humphrey | Alison L. Marsden
Source: Arteriosclerosis Thrombosis and Vascular Biology (review, 2024-04-04)
Field: Materials Science — Biomaterials

Hemodynamics and Wall Mechanics of Vascular Graft Failure Blood vessels are subjected to complex biomechanical loads, primarily from pressure-driven blood flow. Abnormal loading associated with vascular grafts, arising from altered hemodynamics or wall mechanics, can cause acute and progressive vascular failure and end-organ dysfunction. Perturbations to mechanobiological stimuli experienced by vascular cells contribute to remodeling of the vascular wall via activation of mechanosensitive signaling pathways and subsequent changes in gene expression and associated turnover of cells and extracellular matrix. In this review, we outline experimental and computational tools used to quantify metrics of biomechanical loading in vascular grafts and highlight those that show potential in predicting graft failure for diverse disease contexts. We include metrics derived from both fluid and solid mechanics that drive feedback loops between mechanobiological processes and changes in the biomechanical state that govern the natural history of vascular grafts. As illustrative examples, we consider application-specific coronary artery bypass grafts, peripheral vascular grafts, and tissue-engineered vascular grafts for congenital heart surgery as each of these involves unique circulatory environments, loading magnitudes, and graft materials. Mechanosensitive channels ; Mechanobiology ; Hemodynamics ; Circulatory system ; Extracellular matrix ; Blood flow ; Artery ; Medicine ; Cardiology ; Anatomy ; Biology ; Internal medicine ; Cell biology Electrospun Nanofibers in Biomedical Applications ; Coronary Interventions and Diagnostics ; Cardiac and Coronary Surgery Techniques