jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juna Bae | Michiel Dusselier
Source: Chemical Communications (review, 2022-12-22)
Field: Chemistry — Inorganic Chemistry

Synthesis strategies to control the Al distribution in zeolites: thermodynamic and kinetic aspects The activity and selectivity of acid-catalyzed chemistry is highly dependent on the Brønsted and Lewis acid sites generated by Al substitutions in a zeolite framework with the desired pore architecture. The siting of two Al atoms in close proximity in the framework of high-silica zeolites can also play a decisive role in improving the performance of redox catalysts by producing exchangeable positions for extra-framework multivalent cations. Thus, considerable attention has been devoted to controlling the Al incorporation through direct synthesis approaches and post-synthesis treatments to optimize the performance as (industrial) solid catalysts and to develop new acid- and redox-catalyzed reactions. This Feature Article highlights bottom-up synthetic strategies to fine-tune the Al incorporation in zeolites, interpreted with respect to thermodynamic and kinetic aspects. They include (i) variation in extra-framework components in zeolite synthesis, (ii) isomorphous substitution of other heteroatoms in the zeolite framework, and (iii) control over the (alumino)silicate network in the initial synthesis mixture <i>via in situ</i> and <i>ex situ</i> methods. Most synthetic approaches introduced here tentatively showed that the energy barriers associated with Al incorporation in zeolites can be variable during zeolite crystallization processes, occurring in complex media with multiple chemical interactions. Although the generic interpretation of each strategy and underlying crystallization mechanism remains largely unknown (and often limited to a specific framework), this review will provide guidance on more efficient methods to prepare fine-tuned zeolites with desired chemical properties. Zeolite ; Kinetic energy ; Kinetic control ; Chemistry ; Thermodynamics ; Catalysis ; Physics ; Organic chemistry ; Classical mechanics Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis
Authors: Maria Luisa Puga | Janio Venturini | Carla Schwengber ten Caten | Carlos Pérez Bergmann
Source: Ceramics International (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Influencing parameters in the electrochemical anodization of TiO2 nanotubes: Systematic review and meta-analysis Anodizing ; Materials science ; Ammonium fluoride ; Titanium ; Meta-analysis ; Electrochemistry ; Nanotechnology ; Titanium dioxide ; Composite material ; Chemical engineering ; Metallurgy ; Chemistry ; Medicine ; Internal medicine Anodic Oxide Films and Nanostructures ; TiO2 Photocatalysis and Solar Cells ; Ga2O3 and related materials
Authors: Francesca Giulimondi | Elisabetta Vulpis | Luca Digiacomo | Maria Valeria Giuli | Angelica Mancusi | Anna Laura Capriotti | Aldo Laganà | Andrea Cerrato | Riccardo Zenezini Chiozzi | Carmine Nicoletti | Heinz Amenitsch | Francesco Cardarelli | Laura Masuelli | Roberto Bei | Isabella Screpanti | Daniela Pozzi | Alessandra Zingoni | Saula Checquolo | Giulio Caracciolo
Source: ACS Nano (article, 2022-01-18)
Field: Materials Science — Biomaterials

Opsonin-Deficient Nucleoproteic Corona Endows UnPEGylated Liposomes with Stealth Properties <i>In Vivo</i> For several decades, surface grafted polyethylene glycol (PEG) has been a go-to strategy for preserving the synthetic identity of liposomes in physiological milieu and preventing clearance by immune cells. However, the limited clinical translation of PEGylated liposomes is mainly due to the protein corona formation and the subsequent modification of liposomes' synthetic identity, which affects their interactions with immune cells and blood residency. Here we exploit the electric charge of DNA to generate unPEGylated liposome/DNA complexes that, upon exposure to human plasma, gets covered with an opsonin-deficient protein corona. The final product of the synthetic process is a biomimetic nanoparticle type covered by a proteonucleotidic corona, or "proteoDNAsome", which maintains its synthetic identity <i>in vivo</i> and is able to slip past the immune system more efficiently than PEGylated liposomes. Accumulation of proteoDNAsomes in the spleen and the liver was lower than that of PEGylated systems. Our work highlights the importance of generating stable biomolecular coronas in the development of stealth unPEGylated particles, thus providing a connection between the biological behavior of particles <i>in vivo</i> and their synthetic identity. Liposome ; In vivo ; Polyethylene glycol ; Nanotechnology ; PEGylation ; Biophysics ; Materials science ; Immune system ; PEG ratio ; Chemistry ; Biology ; Biochemistry ; Immunology Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Characterization and Applications of Magnetic Nanoparticles
Authors: Sakineh Hajebi | Satar Yousefiasl | Ilnaz Rahimmanesh | Alireza Dahim | Sepideh Ahmadi | Firoz Babu Kadumudi | Nikta Rahgozar | Sanaz Amani | Arun Kumar | Ehsan Kamrani | Mohammad Rabiee | Assunta Borzacchiello | Xiangdong Wang | Navid Rabiee | Alireza Dolatshahi‐Pirouz | Pooyan Makvandi
Source: Advanced Healthcare Materials (review, 2022-08-02)
Field: Materials Science — Biomaterials

Genetically Engineered Viral Vectors and Organic‐Based Non‐Viral Nanocarriers for Drug Delivery Applications Abstract Conventional drug delivery systems are challenged by concerns related to systemic toxicity, repetitive doses, drug concentrations fluctuation, and adverse effects. Various drug delivery systems are developed to overcome these limitations. Nanomaterials are employed in a variety of biomedical applications such as therapeutics delivery, cancer therapy, and tissue engineering. Physiochemical nanoparticle assembly techniques involve the application of solvents and potentially harmful chemicals, commonly at high temperatures. Genetically engineered organisms have the potential to be used as promising candidates for greener, efficient, and more adaptable platforms for the synthesis and assembly of nanomaterials. Genetically engineered carriers are precisely designed and constructed in shape and size, enabling precise control over drug attachment sites. The high accuracy of these novel advanced materials, biocompatibility, and stimuli‐responsiveness, elucidate their emerging application in controlled drug delivery. The current article represents the research progress in developing various genetically engineered carriers. Organic‐based nanoparticles including cellulose, collagen, silk‐like polymers, elastin‐like protein, silk‐elastin‐like protein, and inorganic‐based nanoparticles are discussed in detail. Afterward, viral‐based carriers are classified, and their potential for targeted therapeutics delivery is highlighted. Finally, the challenges and prospects of these delivery systems are concluded. Nanocarriers ; Drug delivery ; Nanotechnology ; Genetically engineered ; Targeted drug delivery ; Drug ; Drug carrier ; Materials science ; Chemistry ; Pharmacology ; Medicine Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials
Authors: Philipp Thölke | Gianni De Fabritiis
Source: arXiv (Cornell University) (preprint, 2022-02-05)
Field: Materials Science — Materials Chemistry

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining computational efficiency comparable with classical force fields. In this work we propose TorchMD-NET, a novel equivariant transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1, and many QM9 targets in both accuracy and computational efficiency. Through an extensive attention weight analysis, we gain valuable insights into the black box predictor and show differences in the learned representation of conformers versus conformations sampled from molecular dynamics or normal modes. Furthermore, we highlight the importance of datasets including off-equilibrium conformations for the evaluation of molecular potentials. Transformer ; Equivariant map ; Computer science ; Quantum ; Artificial neural network ; Artificial intelligence ; Representation (politics) ; Molecular dynamics ; Computational neuroscience ; Machine learning ; Algorithm ; Mathematics ; Physics ; Quantum mechanics ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dorothea Golze | Markus Hirvensalo | Patricia Hernández-León | Anja Aarva | Jarkko Etula | Toma Susi | Patrick Rinke | Tomi Laurila | A. Miguel
Source: Chemistry of Materials (article, 2022-07-13)
Field: Materials Science — Materials Chemistry

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and <i>GW</i> We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which X-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with <i>GW</i> and uses kernel ridge regression for the ML predictions. We apply the new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server. Density functional theory ; Core (optical fiber) ; Electron ; Carbon fibers ; Core electron ; Electron density ; Materials science ; Statistical physics ; Computer science ; Computational physics ; Physics ; Algorithm ; Computational chemistry ; Chemistry ; Quantum mechanics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Shuan Chen | Yousung Jung
Source: Nature Machine Intelligence (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

A generalized-template-based graph neural network for accurate organic reactivity prediction Interpretability ; Computer science ; Template ; Artificial neural network ; Reactivity (psychology) ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sukriti Manna | Troy D. Loeffler | Rohit Batra | Suvo Banik | Henry Chan | Bilvin Varughese | Kiran Sasikumar | Michael Sternberg | Tom Peterka | Mathew J. Cherukara | Stephen K. Gray | Bobby G. Sumpter | Subramanian K. R. S. Sankaranarayanan
Source: Nature Communications (article, 2022-01-18)
Field: Materials Science — Materials Chemistry

Learning in continuous action space for developing high dimensional potential energy models Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have continuous action spaces. Exploring high-dimensional potential energy models of materials is an example. Traditionally, these searches are time consuming (often several years for a single bulk system) and driven by human intuition and/or expertise and more recently by global/local optimization searches that have issues with convergence and/or do not scale well with the search dimensionality. Here, in a departure from discrete action and other gradient-based approaches, we introduce a RL strategy based on decision trees that incorporates modified rewards for improved exploration, efficient sampling during playouts and a "window scaling scheme" for enhanced exploitation, to enable efficient and scalable search for continuous action space problems. Using high-dimensional artificial landscapes and control RL problems, we successfully benchmark our approach against popular global optimization schemes and state of the art policy gradient methods, respectively. We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental structural diversity and the smoothness of the element energy surface. Broadly, our RL strategy will be applicable to many other physical science problems involving search over continuous action spaces. Reinforcement learning ; Computer science ; Curse of dimensionality ; Scalability ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Reinforcement Learning in Robotics ; Protein Structure and Dynamics
Authors: Marina Massaro | Renato Noto | Serena Riela
Source: Catalysts (article, 2022-01-25)
Field: Materials Science — Biomaterials

Halloysite Nanotubes: Smart Nanomaterials in Catalysis The use of clay minerals as catalyst is renowned since ancient times. Among the different clays used for catalytic purposes, halloysite nanotubes (HNTs) represent valuable resources for industrial applications. This special tubular clay possesses high stability and biocompatibility, resistance against organic solvents, and most importantly be available in large amounts at a low cost. Therefore, HNTs can be efficiently used as catalysts themselves or supports for metal nanoparticles in several catalytic processes. This review reports a comprehensive overview of the relevant advances in the use of halloysite in catalysis, focusing the attention on the last five years. Halloysite ; Catalysis ; Nanotechnology ; Nanomaterials ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Composite material Clay minerals and soil interactions ; Layered Double Hydroxides Synthesis and Applications ; Iron oxide chemistry and applications
Authors: Dario Presutti | Tarun Agarwal | Atefeh Zarepour | Nehar Celikkin | Sara Hooshmand | Chinmay Kumar Nayak | Matineh Ghomi | Ali Zarrabi | Marco Costantini | Birendra Behera | Tapas K. Maiti
Source: Materials (review, 2022-01-04)
Field: Materials Science — Materials Chemistry

Transition Metal Dichalcogenides (TMDC)-Based Nanozymes for Biosensing and Therapeutic Applications Nanozymes, a type of nanomaterial with enzyme-like properties, are a promising alternative to natural enzymes. In particular, transition metal dichalcogenides (TMDCs, with the general formula MX<sub>2</sub>, where M represents a transition metal and X is a chalcogen element)-based nanozymes have demonstrated exceptional potential in the healthcare and diagnostic sectors. TMDCs have different enzymatic properties due to their unique nano-architecture, high surface area, and semiconducting properties with tunable band gaps. Furthermore, the compatibility of TMDCs with various chemical or physical modification strategies provide a simple and scalable way to engineer and control their enzymatic activity. Here, we discuss recent advances made with TMDC-based nanozymes for biosensing and therapeutic applications. We also discuss their synthesis strategies, various enzymatic properties, current challenges, and the outlook for future developments in this field. Nanotechnology ; Nanomaterials ; Materials science ; Transition metal ; Biosensor ; Surface modification ; Chemistry ; Catalysis Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Juveriya Parmar | Shobhit K. Patel | Vijay Katkar
Source: Scientific Reports (article, 2022-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene-based metasurface solar absorber design with absorption prediction using machine learning Solar absorber is required to absorb most of the energy of the solar spectral irradiance. We propose a graphene-based solar absorber design with two different metasurfaces to improve this absorption and increase the efficiency of the solar absorber. The metasurfaces are selected based on their symmetrical/asymmetrical nature (O-shape and L-shape). The O-shape metasurface design is showing better performance over the L-shape metasurface design. The absorption performance is also compared with AM 1.5 solar spectral irradiance to show the effectiveness of the solar absorber. The absorption values are also enhanced by varying the parameters like resonator thickness and substrate thickness. The proposed solar absorber design gives maximum absorption in the ultraviolet and visible range. Furthermore, the design is also showing a high and similar absorption rate over a wide angle of incidence. The absorption of O-shape metasurface design is also predicted using machine learning. 1D-Convolutional Neural Network Regression is used to develop a Machine Learning model to determine absorption values of intermediate wavelength for assorted values of angle of incidence, resonator thickness, and substrate thickness. The results of experiments reveal that absorption values may be predicted with a high degree of accuracy. The proposed absorber with its high absorbing capacity can be applied for green energy applications. Absorption (acoustics) ; Materials science ; Substrate (aquarium) ; Optics ; Irradiance ; Wavelength ; Optoelectronics ; Solar energy ; Physics ; Composite material ; Engineering Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Energy Harvesting in Wireless Networks
Authors: Ruiyang Li | Jianxun Wang | Eungkyu Lee | Tengfei Luo
Source: npj Computational Materials (article, 2022-02-08)
Field: Materials Science — Materials Chemistry

Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium Abstract Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large temperature non-equilibrium, we demonstrate a data-free deep learning scheme, physics-informed neural network (PINN), for solving stationary, mode-resolved phonon BTE with arbitrary temperature gradients. This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport (from 1D to 3D) under arbitrary temperature gradients. Moreover, the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design. Phonon ; Boltzmann equation ; Thermal conduction ; Statistical physics ; Temperature gradient ; Boltzmann constant ; Physics ; Relaxation (psychology) ; Representation (politics) ; Computer science ; Condensed matter physics ; Thermodynamics ; Quantum mechanics Thermal properties of materials ; Model Reduction and Neural Networks ; Heat Transfer and Optimization
Authors: Hebat‐Allah S. Tohamy | Mohamed El‐Sakhawy | Samir Kamel
Source: Journal of Fluorescence (article, 2022-11-26)
Field: Materials Science — Materials Chemistry

Eco-friendly Synthesis of Carbon Quantum Dots as an Effective Adsorbent Fluorescent carbon quantum dots (CQDs) were prepared by an economical, green, and single-step procedure with the assistance of microwave heating of urea with bagasse (SCB), cellulose (C), or carboxymethyl cellulose (CMC). The prepared CQDs were characterized using a series of spectroscopic techniques, and they had petite size, intense absorption in the UV, and excitation wavelength-dependent fluorescence. The prepared CQDs were used for Pb(II) adsorption from an aqueous solution. The removal efficiency percentages (R %) were 99.16, 96.36, and 98.48% for QCMC, QC, and QSCB, respectively. The findings validated the efficiency of CQDs synthesized from CMC, cellulose, and SCB as excellent materials for further utilization in the environmental fields of wastewater pollution detection, adsorption, and chemical sensing applications. The kinetics and isotherms studied found that all CQDs isotherms fit well with the Langmuir model than Freundlich and Temkin models. According to R<sup>2</sup>, the pseudo-second-order fits the adsorption of QCMC, while the first-order one fits with QC and QSCB. Chemistry ; Freundlich equation ; Adsorption ; Aqueous solution ; Langmuir ; Cellulose ; Carboxymethyl cellulose ; Absorption (acoustics) ; Bagasse ; Fluorescence ; Carbon fibers ; Chemical engineering ; Nuclear chemistry ; Organic chemistry ; Pulp and paper industry ; Composite material ; Materials science ; Composite number Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Heavy Metal Exposure and Toxicity
Authors: Ting Yang | Zian Jia | Ziling Wu | Hongshun Chen | Zhifei Deng | Liuni Chen | Yunhui Zhu | Ling Li
Source: Nature Communications (article, 2022-10-14)
Field: Materials Science — Biomaterials

High strength and damage-tolerance in echinoderm stereom as a natural bicontinuous ceramic cellular solid Due to their low damage tolerance, engineering ceramic foams are often limited to non-structural usages. In this work, we report that stereom, a bioceramic cellular solid (relative density, 0.2-0.4) commonly found in the mineralized skeletal elements of echinoderms (e.g., sea urchin spines), achieves simultaneous high relative strength which approaches the Suquet bound and remarkable energy absorption capability (ca. 17.7 kJ kg<sup>-1</sup>) through its unique bicontinuous open-cell foam-like microstructure. The high strength is due to the ultra-low stress concentrations within the stereom during loading, resulted from their defect-free cellular morphologies with near-constant surface mean curvatures and negative Gaussian curvatures. Furthermore, the combination of bending-induced microfracture of branches and subsequent local jamming of fractured fragments facilitated by small throat openings in stereom leads to the progressive formation and growth of damage bands with significant microscopic densification of fragments, and consequently, contributes to stereom's exceptionally high damage tolerance. Echinoderm ; Ceramic ; Damage tolerance ; Materials science ; Composite material ; Biology ; Ecology ; Composite number Calcium Carbonate Crystallization and Inhibition ; Marine Biology and Environmental Chemistry ; Echinoderm biology and ecology