jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiulin Wang | Xiaoyu Wang | Yakai Feng
Source: Gels (review, 2023-05-01)
Field: Materials Science — Biomaterials

Chitosan Hydrogel as Tissue Engineering Scaffolds for Vascular Regeneration Applications Chitosan hydrogels have a wide range of applications in tissue engineering scaffolds, mainly due to the advantages of their chemical and physical properties. This review focuses on the application of chitosan hydrogels in tissue engineering scaffolds for vascular regeneration. We have mainly introduced these following aspects: advantages and progress of chitosan hydrogels in vascular regeneration hydrogels and the modification of chitosan hydrogels to improve the application in vascular regeneration. Finally, this paper discusses the prospects of chitosan hydrogels for vascular regeneration. Self-healing hydrogels ; Chitosan ; Regeneration (biology) ; Tissue engineering ; Materials science ; Biomedical engineering ; Nanotechnology ; Chemistry ; Polymer chemistry ; Cell biology ; Engineering ; Biochemistry ; Biology Electrospun Nanofibers in Biomedical Applications ; Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications
Authors: Feng Xie | Liang Yu | Hao Wang | Jing Li
Source: Angewandte Chemie International Edition (review, 2023-02-16)
Field: Chemistry — Inorganic Chemistry

Metal‐Organic Frameworks for C6 Alkane Separation The separation of alkane isomers is an important yet challenging process in the petrochemical industry. Being a crucial step to produce premium gasoline components as well as optimum ethylene feed, the current industrial separation by distillation is extremely energy intensive. Adsorptive separation based on zeolite is limited by insufficient adsorption capacity. Metal-organic frameworks (MOFs) hold enormous promise as alternative adsorbents due to their diverse structural tunability and exceptional porosity. Precise control of their pore geometry/dimensions has led to superior performance. In this minireview, we highlight the recent progresses in developing MOFs for the separation of C6 alkane isomers. Representative MOFs are reviewed based on their separation mechanisms. Emphasis is put on the material design rationale for achieving optimal separation capability. Finally, we briefly discuss the existing challenges, possible solutions, and future directions of this important field. Alkane ; Separation (statistics) ; Metal-organic framework ; Chemistry ; Environmental science ; Environmental chemistry ; Chromatography ; Organic chemistry ; Computer science ; Hydrocarbon ; Adsorption ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications
Authors: Alberto F. Garrido‐Castro | Yu̅ta Hioki | Yoshifumi Kusumoto | Kyohei Hayashi | Jeremy Griffin | Kaid C. Harper | Yu Kawamata | Phil S. Baran
Source: Angewandte Chemie International Edition (article, 2023-09-01)
Field: Chemistry — Organic Chemistry

Scalable Electrochemical Decarboxylative Olefination Driven by Alternating Polarity** A mild, scalable (kg) metal-free electrochemical decarboxylation of alkyl carboxylic acids to olefins is disclosed. Numerous applications are presented wherein this transformation can simplify alkene synthesis and provide alternative synthetic access to valuable olefins from simple carboxylic acid feedstocks. This robust method relies on alternating polarity to maintain the quality of the electrode surface and local pH, providing a deeper understanding of the Hofer-Moest process with unprecedented chemoselectivity. Decarboxylation ; Chemoselectivity ; Polarity (international relations) ; Alkene ; Scalability ; Electrochemistry ; Carboxylic acid ; Combinatorial chemistry ; Alkyl ; Chemistry ; Computer science ; Materials science ; Organic chemistry ; Electrode ; Catalysis Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts ; Catalytic C–H Functionalization Methods
Authors: Markus Borsch | Manuel Meierhofer | R. Huber | M. Kira
Source: Nature Reviews Materials (review, 2023-09-15)
Field: Chemistry — Spectroscopy

Lightwave electronics in condensed matter Key properties of quantum materials stem from dynamic interaction chains that connect stable electronic quasiparticles through short-lived coherences, which are difficult to control at their natural time and length scales. Lightwave electronics sculpts the quantum flow of electrons and coherences faster than an oscillation cycle of light by using intense optical-carrier waves as fast biasing fields, which can access multi-electron interaction chains. In this Review, we summarize the key functionalities and the latest advances in lightwave electronics for both fundamental and technological explorations. For example, lightwave-driven ballistic electron transport through dynamically changing band structures has already led to the demonstration of phenomena such as high-harmonic emission and dynamic Bloch oscillations. Lightwave electronic control could also seamlessly convert quantum states between light and matter to create quantum chips that simultaneously exploit electronics for efficient interactions and optics for speed or long coherence lifetimes. Additionally, we present an outlook towards applications of lightwave electronics including quasiparticle colliders to explore quantum phenomena; all-optical band-structure reconstruction in ambient conditions; attoclocks to measure the interaction dynamics of diverse quantum phenomena; ultrafast electron videography to watch electronic reactions unfold; efficient light sources to create compact integration; and petahertz electronics to speed up traditional semiconductor electronics. Lightwave electronics could enable the control of interactions in quantum materials and provide access to the quantum phases and quantum information of condensed-matter systems. This Review discusses the fundamental concepts of lightwave electronics and outlines key advances and potential applications. Electronics ; Quantum optics ; Physics ; Quantum ; Quantum technology ; Quantum dot ; Quasiparticle ; Optoelectronics ; Nanotechnology ; Electrical engineering ; Open quantum system ; Optics ; Materials science ; Quantum mechanics ; Engineering Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Laser-Matter Interactions and Applications
Authors: Zezhu Zeng | Felix Wodaczek | Keyang Liu | Frederick Stein | Jürg Hutter | Ji Chen | Bingqing Cheng
Source: Nature Communications (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations Water adsorption and dissociation processes on pristine low-index TiO<sub>2</sub> interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO<sub>2</sub> surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO<sub>2</sub> surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO<sub>2</sub> surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces. Anatase ; Rutile ; Dissociation (chemistry) ; Adsorption ; Molecular dynamics ; Materials science ; Chemical physics ; Density functional theory ; Molecule ; Chemical engineering ; Computational chemistry ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Albert Zhu | Simon Batzner | Albert Musaelian | Boris Kozinsky
Source: The Journal of Chemical Physics (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Fast uncertainty estimates in deep learning interatomic potentials Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and material properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction, resulting in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost. Computer science ; Inference ; Artificial neural network ; Machine learning ; Artificial intelligence ; Ensemble forecasting ; Uncertainty quantification ; Deep learning ; Overhead (engineering) ; Ensemble learning ; Quality (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tomer Weiss | Eduardo Mayo Yanes | Sabyasachi Chakraborty | Luca Cosmo | Alex Bronstein | Renana Gershoni‐Poranne
Source: Nature Computational Science (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

Guided diffusion for inverse molecular design Computer science ; Scalability ; Holy Grail ; Inverse ; Generative model ; Molecular graph ; Function (biology) ; Generative grammar ; Deep learning ; Artificial intelligence ; Graph ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Anam Zahra | Rizwan Qureshi | Muhammad Sajjad | Ferhat Sadak | Mehmood Nawaz | Haris Ahmad Khan | Muhammad Uzair
Source: Expert Systems with Applications (article, 2023-10-16)
Field: Chemistry — Analytical Chemistry

Current advances in imaging spectroscopy and its state-of-the-art applications Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy. Computer science ; Imaging spectroscopy ; Spectroscopy ; Software ; Imaging science ; Molecular spectroscopy ; Data science ; Systems engineering ; Artificial intelligence ; Engineering ; Hyperspectral imaging ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Jonathan Suazo-Hernández | Nicolás Arancibia‐Miranda | Rawan Mlih | Lizethly Cáceres-Jensen | Nanthi Bolan | Marı́a de la Luz Mora
Source: Nanomaterials (review, 2023-01-31)
Field: Materials Science — Materials Chemistry

Impact on Some Soil Physical and Chemical Properties Caused by Metal and Metallic Oxide Engineered Nanoparticles: A Review In recent years, the release of metal and metallic oxide engineered nanoparticles (ENPs) into the environment has generated an increase in their accumulation in agricultural soils, which is a serious risk to the ecosystem and soil health. Here, we show the impact of ENPs on the physical and chemical properties of soils. A literature search was performed in the Scopus database using the keywords ENPs, plus soil physical properties or soil chemical properties, and elements availability. In general, we found that the presence of metal and metallic oxide ENPs in soils can increase hydraulic conductivity and soil porosity and reduce the distance between soil particles, as well as causing a variation in pH, cation exchange capacity (CEC), electrical conductivity (EC), redox potential (Eh), and soil organic matter (SOM) content. Furthermore, ENPs or the metal cations released from them in soils can interact with nutrients like phosphorus (P) forming complexes or precipitates, decreasing their bioavailability in the soil solution. The results depend on the soil properties and the doses, exposure duration, concentrations, and type of ENPs. Therefore, we suggest that particular attention should be paid to every kind of metal and metallic oxide ENPs deposited into the soil. Cation-exchange capacity ; Soil water ; Environmental chemistry ; Metal ; Organic matter ; Oxide ; Soil organic matter ; Materials science ; Chemistry ; Environmental science ; Chemical engineering ; Soil science ; Metallurgy Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Graphene and Nanomaterials Applications
Authors: Sascha Klawohn | James P. Darby | James R. Kermode | Gábor Cśanyi | A. Miguel | Albert P. Bartók
Source: The Journal of Chemical Physics (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Gaussian approximation potentials: Theory, software implementation and application examples Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements. Computer science ; Software ; Gaussian ; Scaling ; Computational science ; Code (set theory) ; Class (philosophy) ; Interatomic potential ; Interface (matter) ; Algorithm ; Ab initio ; Theoretical computer science ; Statistical physics ; Parallel computing ; Artificial intelligence ; Molecular dynamics ; Physics ; Computational chemistry ; Programming language ; Mathematics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Chenru Duan | Yuanqi Du | Haojun Jia | Heather J. Kulik
Source: Nature Computational Science (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model Statistical physics ; Computer science ; Transition state theory ; Quantum ; Reaction rate ; Transition state ; Equivariant map ; Chemistry ; Theoretical physics ; Mathematics ; Physics ; Quantum mechanics ; Reaction rate constant ; Pure mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kousain Kousar | Faiza Naseer | Maisa Siddiq Abduh | Salik Javed Kakar | Rabia Gul | Sadia Anjum | Tahir Ahmad
Source: Frontiers in Pharmacology (article, 2023-01-12)
Field: Materials Science — Biomaterials

Green synthesis of hyaluronic acid coated, thiolated chitosan nanoparticles for CD44 targeted delivery and sustained release of Cisplatin in cervical carcinoma Cervical carcinoma is one of the most prevalent gynecological cancers throughout the world. Cisplatin is used as first line chemotherapy for treatment of cervical cancer, but it comes with plethora of side effects. The aim of this study was to develop hyaluronic acid coated, thiolated chitosan nanocarriers using green synthesis approach, for CD44 targeted delivery and sustained release of Cisplatin in cervical cancer cells. After synthesis through ionic gelation method, Zeta analysis showed that the nanoparticle size was 265.9 nm with a zeta potential of +22.3 mV and .226 PDI. SEM and TEM analysis confirmed the spherical shape and smooth surface of nanoparticles. FTIR and XRD showed the presence of characteristic functional groups, successful encapsulation of drug, and crystalline nature of nanoparticles respectively. Drug loading and entrapment efficiency were calculated to be 70.1% <b>±</b> 1.2% and 45% ± .28% respectively. Analysis of <i>in vitro</i> drug release kinetics showed that drug release followed the Higuchi model at pH 6.8 and 7.4 and Cisplatin release for up to 72 h confirmed sustained release. <i>In vitro</i> analysis on cervical cancer cells HeLa and normal cervical epithelial cells HCK1T was done through cell morphology analysis, trypan blue assay (concentration range of 10-80 μg/ml), and MTT cytotoxic assay (concentration range of 10-90 μg/ml). The results showed a higher cytotoxic potential of HA coated, thiolated chitosan encapsulated Cisplatin (HA-ThCs-Cis NP) nanoformulation as compared to pure Cisplatin in HeLa while in HCK1T, pure Cisplatin showed much higher toxicity as compared to HA-ThCs-Cis nanoformulation. These findings suggest that CD44 targeted delivery system can be a useful approach to minimize offtarget toxicities, give sustained release and better cellular uptake in cancer cells. HeLa ; Cisplatin ; Chitosan ; Hyaluronic acid ; Nanocarriers ; MTT assay ; Zeta potential ; Chemistry ; CD44 ; Drug delivery ; Pharmacology ; Nanoparticle ; In vitro ; Biophysics ; Nuclear chemistry ; Biochemistry ; Medicine ; Materials science ; Nanotechnology ; Chemotherapy ; Organic chemistry ; Surgery ; Biology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Proteoglycans and glycosaminoglycans research
Authors: Sina Momeni | Kaylee Craplewe | Muhammad Safder | Sandra M. Luz | Dominic Sauvageau | Anastasia Elias
Source: ACS Sustainable Chemistry & Engineering (review, 2023-10-10)
Field: Materials Science — Biomaterials

Accelerating the Biodegradation of Poly(lactic acid) through the Inclusion of Plant Fibers: A Review of Recent Advances As the global demand for plastics continues to grow, plastic waste is accumulating at an alarming rate with negative effects on the natural environment. The industrially compostable biopolymer poly(lactic acid) (PLA) is therefore being adopted for use in many applications, but the degradation of this material is slow under many end-of-life conditions. This Perspective explores the feasibility of accelerating the degradation of PLA through the formation of PLA-plant fiber composites. Topics include: (a) key properties of PLA, plant-based fibers, and biocomposites; (b) mechanisms of both hydrolytic degradation and biodegradation of PLA-fiber composites; (c) end-of-life degradation of PLA and PLA-plant fiber composites in aerobic and anaerobic conditions, relevant to compost, soil and seawater (aerobic), and landfills (anaerobic); and (d) sustainability and environmental impact of PLA and PLA-plant fiber composites, as evaluated using life cycle assessment. Additional degradation modes, including thermal and photodegradation, which are relevant during processing and use, have been omitted for clarity, as have other types of PLA biocomposites. Multiple studies have shown that the addition of some types of plant fibers to PLA (to form PLA biocomposites) accelerates both water transport in the material and hydrolysis, presenting a possible avenue for improving the end-of-life degradation of these materials. To facilitate the continued development of materials with enhanced biodegradability, we identify a need to implement testing protocols that can distinguish between different degradation mechanisms. Biodegradation ; Degradation (telecommunications) ; Hydrolytic degradation ; Materials science ; Polylactic acid ; Polyester ; Hydrolysis ; Biopolymer ; Lactic acid ; Fiber ; Pulp and paper industry ; Composite material ; Chemistry ; Polymer ; Organic chemistry ; Computer science biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Microplastics and Plastic Pollution
Authors: Hao Xu | Jinglong Lin | Dongxiao Zhang | Fanyang Mo
Source: Nature Communications (article, 2023-05-29)
Field: Chemistry — Spectroscopy

Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery. Retention time ; Artificial neural network ; Computer science ; Quantile ; Enantiomer ; Artificial intelligence ; Machine learning ; Task (project management) ; Chromatography ; Chemistry ; Mathematics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies