jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Shen | Eric M. Lynch | Susrut Akkineni | Joseph L. Watson | Justin Decarreau | Neville P. Bethel | Issa Benna | William Sheffler | Daniel P. Farrell | Frank DiMaio | Emmanuel Derivery | James J. De Yoreo | Justin M. Kollman | David Baker
Source: Nature Nanotechnology (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

De novo design of pH-responsive self-assembling helical protein filaments Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes. Nanotechnology ; Protein design ; Biophysics ; Self-assembly ; Materials science ; Chemistry ; Crystallography ; Protein structure ; Biochemistry ; Biology Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Biochemical and Molecular Research
Authors: Thibaut Dumas | Roxana Martinez Pinna | Clément Lozano | Sonja Radau | Olivier Pible | Lucia Grenga | Jean Armengaud
Source: Microbiome (article, 2024-03-07)
Field: Chemistry — Spectroscopy

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes Our approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes. Video Abstract. Metaproteomics ; Orbitrap ; Computational biology ; Metagenomics ; Biology ; Computer science ; Mass spectrometry ; Sample (material) ; Spectrum analyzer ; Biological system ; Data mining ; Chemistry ; Chromatography ; Genetics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yijie Shen | Chao He | Zipei Song | Binguo Chen | Honghui He | Yifei Ma | Julian Fells | Steve J. Elston | Stephen Morris | Martin J. Booth | Andrew Forbes
Source: Physical Review Applied (article, 2024-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Topologically controlled multiskyrmions in photonic gradient-index lenses Skyrmions are topologically protected quasiparticles, originally studied in condensed-matter systems and recently in photonics, with great potential in high-density information storage. Despite the recent attention, most optical solutions require complex systems yet produce limited topologies. Here we demonstrate an extended family of quasiparticles beyond normal skyrmions that are controlled in compact photonic gradient-index media, extending to higher-order members such as multiskyrmions and multimerons, with increasingly complex topologies. We introduce multiple topological numbers (centrality, radiality, vorticity, and polarity) in addition to the skyrmion number to describe these photonic quasiparticles. Our compact creation system lends itself to integrated and programmable solutions of complex particle textures, with potential impacts on both photonic and condensed-matter systems for revolutionizing topological informatics and logic devices. Index (typography) ; Photonics ; Optics ; Physics ; Materials science ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Ahmed E. Alprol | Ahmed Eleryan | Ahmed Abouelwafa | Ahmed M. Gad | Tarek M. Hamad
Source: Scientific Reports (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Green synthesis of zinc oxide nanoparticles using Padina pavonica extract for efficient photocatalytic removal of methylene blue Dye-laden wastewater poses a significant environmental and health threat. This study investigated the potential of green-synthesized zinc oxide nanoparticles (ZnO NPs), derived from Padina pavonica brown algae extract, for the removal of methylene blue (MB) dye. The hypothesis was that utilizing algal extract for ZnO NP synthesis would enhance adsorption capacity and photocatalytic activity for dye removal. The synthesized ZnO NPs, characterized by Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX) and Zeta Potential, demonstrated high adsorption capacity (Qm = 192.308 mg g<sup>-1</sup>) and excellent removal efficiency (> 98%) for MB at low dye concentrations. Langmuir isotherm and pseudo-second-order kinetic models best fit the experimental data, suggesting monolayer adsorption and chemisorption as the primary mechanisms. Notably, the green ZnO NPs exhibited greater photocatalytic activity under direct sunlight irradiation compared to other light sources. Additionally, these nanoparticles displayed antimicrobial properties against various bacteria, indicating potential for water disinfection. This research offers a sustainable and environmentally friendly approach for wastewater treatment utilizing green ZnO NPs for efficient dye removal and potential water disinfection applications. Methylene blue ; Photocatalysis ; Zeta potential ; Adsorption ; Langmuir adsorption model ; Nuclear chemistry ; Chemisorption ; Zinc ; Fourier transform infrared spectroscopy ; Nanoparticle ; Chemistry ; Brilliant green ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Catalysis Nanoparticles: synthesis and applications ; Marine and Coastal Ecosystems
Authors: Ayesha Afrin | Chinna Ayya Swamy P
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Symphony of light: AIE and MFC in carbazole-based cyanostilbenes Carbazole–cyanostilbene conjugates exhibit promising optical properties, including AIE and MFC. This review explores their diverse applications across various fields, highlighting their unique characteristics and potential impact. Carbazole ; Symphony ; Materials science ; Nanotechnology ; Optoelectronics ; Photochemistry ; Chemistry ; Art history ; Art Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics ; Organic Light-Emitting Diodes Research
Authors: Mateusz Jamroży | Sonia Kudłacik‐Kramarczyk | Anna Drabczyk | Marcel Krzan
Source: International Journal of Molecular Sciences (review, 2024-01-08)
Field: Materials Science — Biomaterials

Advanced Drug Carriers: A Review of Selected Protein, Polysaccharide, and Lipid Drug Delivery Platforms Studies on bionanocomposite drug carriers are a key area in the field of active substance delivery, introducing innovative approaches to improve drug therapy. Such drug carriers play a crucial role in enhancing the bioavailability of active substances, affecting therapy efficiency and precision. The targeted delivery of drugs to the targeted sites of action and minimization of toxicity to the body is becoming possible through the use of these advanced carriers. Recent research has focused on bionanocomposite structures based on biopolymers, including lipids, polysaccharides, and proteins. This review paper is focused on the description of lipid-containing nanocomposite carriers (including liposomes, lipid emulsions, lipid nanoparticles, solid lipid nanoparticles, and nanostructured lipid carriers), polysaccharide-containing nanocomposite carriers (including alginate and cellulose), and protein-containing nanocomposite carriers (e.g., gelatin and albumin). It was demonstrated in many investigations that such carriers show the ability to load therapeutic substances efficiently and precisely control drug release. They also demonstrated desirable biocompatibility, which is a promising sign for their potential application in drug therapy. The development of bionanocomposite drug carriers indicates a novel approach to improving drug delivery processes, which has the potential to contribute to significant advances in the field of pharmacology, improving therapeutic efficacy while minimizing side effects. Drug carrier ; Drug ; Solid lipid nanoparticle ; Drug delivery ; Liposome ; Biocompatibility ; Pharmacology ; Targeted drug delivery ; Chemistry ; Bioavailability ; Nanotechnology ; Materials science ; Medicine ; Organic chemistry Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Drug Solubulity and Delivery Systems
Authors: Wenbo Dai | Yitian Jiang | Yunxiang Lei | Xiaobo Huang | Peng Sun | Jianbing Shi | Bin Tong | Dongpeng Yan | Zhengxu Cai | Yuping Dong
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Recent progress in ion-regulated organic room-temperature phosphorescence Organic room-temperature phosphorescence (RTP) materials have attracted considerable attention for their extended afterglow at ambient conditions, eco-friendliness, and wide-ranging applications in bio-imaging, data storage, security inks, and emergency illumination. Significant advancements have been achieved in recent years in developing highly efficient RTP materials by manipulating the intermolecular interactions. In this perspective, we have summarized recent advances in ion-regulated organic RTP materials based on the roles and interactions of ions, including the ion-π interactions, electrostatic interactions, and coordinate interactions. Subsequently, the current challenges and prospects of utilizing ionic interactions for inducing and modulating the phosphorescent properties are presented. It is anticipated that this perspective will provide basic guidelines for fabricating novel ionic RTP materials and further extend their application potential. Phosphorescence ; Ion ; Perspective (graphical) ; Nanotechnology ; Chemical physics ; Materials science ; Chemistry ; Physics ; Organic chemistry ; Computer science ; Fluorescence ; Optics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Organic Light-Emitting Diodes Research
Authors: Yu Fei Pan | Tao Jian | Pingfan Gu | Yiwen Song | Qi Wang | Bo Han | Yuqia Ran | Zemin Pan | Yanping Li | Wanjin Xu | Peng Gao | Chendong Zhang | Jun He | Xiaolong Xu | Yu Ye
Source: Nature Communications (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Precise p-type and n-type doping of two-dimensional semiconductors for monolithic integrated circuits The controllable fabrication of patterned p-type and n-type channels with precise doping control presents a significant challenge, impeding the realization of complementary metal-oxide-semiconductor (CMOS) logic using a single van der Waals material. However, such an achievement could offer substantial benefits by enabling continued transistor scaling and unprecedented interlayer interconnect technologies. In this study, we devise a precise method for two-dimensional (2D) semiconductor substitutional doping, which allows for the production of wafer-scale 2H-MoTe<sub>2</sub> thin films with specific p-type or n-type doping. Notably, we extend this approach to the synthesis of spatially selective doped 2H-MoTe<sub>2</sub> thin films via a one-step growth method, facilitating the monolithic integration of p-type and n-type semiconductor channels. Leveraging this advancement, we successfully fabricate a chip-sized 2D CMOS inverter array that demonstrates excellent device performance and yield. Collectively, these findings represent a significant stride towards the practical incorporation of 2D semiconductors in very large-scale integration technology. Integrated circuit ; Doping ; Semiconductor ; Type (biology) ; Materials science ; Electronic circuit ; Optoelectronics ; Physics ; Biology ; Quantum mechanics Graphene research and applications ; 2D Materials and Applications ; Advancements in Semiconductor Devices and Circuit Design
Authors: Islam Hamad | Amani A. Harb | Yasser Bustanji
Source: Pharmaceutics (article, 2024-03-14)
Field: Materials Science — Biomaterials

Liposome-Based Drug Delivery Systems in Cancer Research: An Analysis of Global Landscape Efforts and Achievements Lipid-bilayer-based liposomes are gaining attention in scientific research for their versatile role in drug delivery. With their amphiphilic design, liposomes efficiently encapsulate and deliver drugs to targeted sites, offering controlled release. These artificial structures hold great promise in advancing cancer therapy methodologies. Bibliometric research analyzes systematic literary data statistically. This study used bibliometric indicators to examine, map, and evaluate the applications of liposomes in cancer therapy. A Scopus search was conducted to identify all English-language peer-reviewed scientific publications on the applications of liposomes in cancer therapy within the past twenty years. Bibliometric indicators were calculated using VOSviewer and Biblioshiny. We produced thematic, conceptual, and visualization charts. A total of 14,873 published documents were obtained. The procedure of keyword mapping has effectively identified the main areas of research concentration and prevailing trends within this specific field of study. The significant clusters discovered through theme and hotspot analyses encompassed many topics such as the use of multiple strategies in chemotherapy and different forms of cancer, the study of pharmacokinetics and nanomedicine, as well as the investigation of targeted drug delivery, cytotoxicity, and gene delivery. Liposomes were employed as drug delivery systems so as to selectively target cancer cells and improve the bioavailability of anticancer drugs. The work showcased the capacity to tailor these liposomes for accurate drug delivery by including potent anticancer medications. Our findings not only bring attention to the latest progress in utilizing liposomes for cancer treatment but also underscore the vital need for ongoing research, collaborative efforts, and the effective translation of these breakthroughs into tangible clinical applications, emphasizing the dynamic and evolving nature of cancer therapeutics. Liposome ; Drug delivery ; Nanomedicine ; Medicine ; Computer science ; Nanotechnology ; Pharmacology ; Data science ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Extracellular vesicles in disease
Authors: Jiadong Chen | Samuel R. Cross | Lincoln J. Miara | Jeong‐Ju Cho | Yan Wang | Wenhao Sun
Source: Nature Synthesis (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Navigating phase diagram complexity to guide robotic inorganic materials synthesis Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists. Realization (probability) ; Battery (electricity) ; Phase (matter) ; Nanotechnology ; Materials science ; Phase diagram ; Yield (engineering) ; Electrolyte ; Computer science ; Process engineering ; Chemistry ; Thermodynamics ; Power (physics) ; Engineering ; Organic chemistry ; Physics ; Physical chemistry ; Mathematics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Catalysis and Oxidation Reactions
Authors: Suparna Das | Hirak Mazumdar | Kamil Reza Khondakar | Yogendra Kumar Mishra | Ajeet Kaushik
Source: ECS Sensors Plus (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Review—Quantum Biosensors: Principles and Applications in Medical Diagnostics Originating at the intersection of physics and biosensing, quantum biosensors (QB) are transforming medical diagnostics and personalized medicine by exploiting quantum phenomena to amplify sensitivity, specificity, and detection speed compared to traditional biosensors. Their foundation lies in the fusion of biological entities like DNA, proteins, or enzymes with quantum sensors, which elicits discernible alterations in light emissions when interacting with sample molecules. Their prowess in identifying disease-linked biomarkers presents an avenue for early diagnoses of conditions like Alzheimer’s and cancer. Beyond this, they enable real-time monitoring of treatment responses by capturing the dynamism of biomarkers, but QB still faces challenges, such as issues of stability, reproducibility, and intricate quantum interactions. Moreover, seamless integration into prevailing diagnostic frameworks necessitates careful consideration. Looking ahead, the evolution of QB navigates uncharted territories. Innovations in fabrication techniques, interdisciplinary collaborations, and standardization protocols emerge as pivotal areas of exploration. This comprehensive discourse encapsulates QB’s principles, diverse iterations, and burgeoning medical utilities. It delves into inherent challenges and limitations, shedding light on the potential trajectories of future research. As QB continues to evolve, its potential to redefine medical diagnostics becomes increasingly tangible. The saga of QB resonates with possibilities, poised to reshape the diagnostic landscape profoundly. Nanotechnology ; Biosensor ; Computer science ; Materials science Diamond and Carbon-based Materials Research ; Electrochemical sensors and biosensors ; Carbon Nanotubes in Composites
Authors: Pavlo O. Dral | Fuchun Ge | Yi-Fan Hou | Peikun Zheng | Yuxinxin Chen | Mario Barbatti | Olexandr Isayev | Cheng Wang | Bao-Xin Xue | Max Pinheiro | Yuming Su | Yiheng Dai | Yangtao Chen | Lina Zhang | Shuang Zhang | Arif Ullah | Quanhao Zhang | Yanchi Ou
Source: Journal of Chemical Theory and Computation (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries. Computer science ; Workflow ; Python (programming language) ; Computational science ; Software ; Leverage (statistics) ; Scripting language ; Cloud computing ; Artificial intelligence ; Database ; Programming language ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ryan Jacobs | Jian Liu | Harry Abernathy | Dane Morgan
Source: Advanced Energy Materials (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Machine Learning Design of Perovskite Catalytic Properties Abstract Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen diffusivity, and area specific resistance (ASR). The models are based on trivial‐to‐calculate elemental features and are more accurate and dramatically faster than the best models based on ab initio‐derived features, potentially eliminating the need for ab initio calculations in descriptor‐based screening. The model of ASR enables temperature‐dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross‐validation reveals the model to be effective at discovering new promising materials prior to their initial discovery, demonstrating the model can make meaningful predictions. Using the SHapley Additive ExPlanations (SHAP) approach, detailed discussion of different approaches of model featurization is provided for ML property prediction. Finally, the model is used to screen more than 19 million perovskites to develop a list of promising cheap, earth‐abundant, stable, and high performing materials, and find some top materials contain mixtures of less‐explored elements (e.g., K, Bi, Y, Ni, Cu) worth exploring in more detail. Perovskite (structure) ; Ab initio ; Materials science ; Oxide ; Fuel cells ; Oxygen ; Computer science ; Catalysis ; Thermal diffusivity ; Thermodynamics ; Biological system ; Chemical engineering ; Physics ; Chemistry ; Metallurgy Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides