jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongjoon Rhee | Deep Jariwala | Jeong Ho Cho | Joohoon Kang
Source: Applied Physics Reviews (article, 2024-04-18)
Field: Materials Science — Materials Chemistry

Solution-processed 2D van der Waals networks: Fabrication strategies, properties, and scalable device applications Solution-based processing of two-dimensional (2D) materials has garnered significant interest as a facile and versatile route for the large-scalable production of 2D material films. Despite the benefits in process, these films were not considered suitable for device applications during the early stages of research because their electronic properties were far from those of 2D materials obtained through micromechanical exfoliation or chemical vapor deposition. Due to the small lateral dimensions and polydisperse thickness of constituent 2D nanosheets, the resulting film tends to be porous and exhibits numerous inter-sheet junctions, primarily contacting edge-to-edge. This nanosheet morphology leads to poor electrical conductivity of the network, and also hinders the film functioning as a semiconductor or an insulator. To produce ultrathin 2D nanosheets with narrow thickness distribution and large lateral sizes, various chemical exfoliation strategies have been explored, but these are limited by long process times, involvement of harsh chemicals, and/or undesired structural damage or phase changes. Recent breakthroughs in electrochemical exfoliation using tetraalkylammonium intercalants enabled the production of high-quality 2D nanosheets with structural characteristics favorable for producing ultrathin, conformal films of 2D materials, which allow for scalable production of high-performance electronic components that can readily be assembled into functional devices via solution-processing. In this review article, we aim to offer an extensive introduction solution-based processing techniques for acquiring 2D nanosheets, their subsequent assembly into thin films, and their diverse applications, primarily focusing on electronics and optoelectronics but also extending to other fields. Remaining challenges and potential avenues for advancement will also be discussed. van der Waals force ; Fabrication ; Scalability ; Materials science ; Nanotechnology ; Computer science ; Physics ; Molecule ; Database ; Quantum mechanics 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Pei-Hua Ma | Xiaoxue Jia | Xin Zhang | Yue Li | Yiyang He | Tangyuan Li | Christine J. Wu | Yong Hoon Joo | Seong-Ho Lee | Taotao Meng | Alexandra H. Brozena | Stephanie Li | Qin Wang | Cheng–I Wei | Robert M. Briber | Yimin Mao | Liangbing Hu
Source: Matter (article, 2024-06-24)
Field: Materials Science — Biomaterials

Ion-chelated porous chitosan nanocrystal for highly efficient postharvest preservation Nanocrystal ; Chitosan ; Postharvest ; Chelation ; Ion ; Porosity ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Metallurgy ; Composite material ; Organic chemistry ; Horticulture ; Engineering ; Biology Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications ; Nanoparticles: synthesis and applications
Authors: Nargish Parvin | Vineet Kumar | Tapas Kumar Mandal | Sang Woo Joo
Source: Journal of Functional Biomaterials (review, 2024-08-14)
Field: Materials Science — Materials Chemistry

Advancements in Nanoporous Materials for Biomedical Imaging and Diagnostics This review explores the latest advancements in nanoporous materials and their applications in biomedical imaging and diagnostics. Nanoporous materials possess unique structural features, including high surface area, tunable pore size, and versatile surface chemistry, making them highly promising platforms for a range of biomedical applications. This review begins by providing an overview of the various types of nanoporous materials, including mesoporous silica nanoparticles, metal-organic frameworks, carbon-based materials, and nanoporous gold. The synthesis method for each material, their current research trends, and prospects are discussed in detail. Furthermore, this review delves into the functionalization and surface modification techniques employed to tailor nanoporous materials for specific biomedical imaging applications. This section covers chemical functionalization, bioconjugation strategies, and surface coating and encapsulation methods. Additionally, this review examines the diverse biomedical imaging techniques enabled by nanoporous materials, such as fluorescence imaging, magnetic resonance imaging (MRI), computed tomography (CT) imaging, ultrasound imaging, and multimodal imaging. The mechanisms underlying these imaging techniques, their diagnostic applications, and their efficacy in clinical settings are thoroughly explored. Through an extensive analysis of recent research findings and emerging trends, this review underscores the transformative potential of nanoporous materials in advancing biomedical imaging and diagnostics. The integration of interdisciplinary approaches, innovative synthesis techniques, and functionalization strategies offers promising avenues for the development of next-generation imaging agents and diagnostic tools with enhanced sensitivity, specificity, and biocompatibility. Nanoporous ; Nanotechnology ; Surface modification ; Materials science ; Bioconjugation ; Medical imaging ; Computer science ; Artificial intelligence ; Chemistry Nanoporous metals and alloys ; Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Silvana Alfei | Omar Ginoble Pandoli
Source: Journal of Xenobiotics (review, 2024-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bamboo-Based Biochar: A Still Too Little-Studied Black Gold and Its Current Applications Biochar (BC), also referred to as "black gold", is a carbon heterogeneous material rich in aromatic systems and minerals, preparable by the thermal decomposition of vegetable and animal biomasses in controlled conditions and with clean technology. Due to its adsorption ability and presence of persistent free radicals (PFRs), BC has demonstrated, among other uses, great potential in the removal of environmental organic and inorganic xenobiotics. Bamboo is an evergreen perennial flowering plant characterized by a short five-year growth period, fast harvesting, and large production in many tropical and subtropical countries worldwide, thus representing an attractive, low-cost, eco-friendly, and renewable bioresource for producing BC. Due to their large surface area and increased porosity, the pyrolyzed derivatives of bamboo, including bamboo biochar (BBC) or activated BBC (ABBC), are considered great bio-adsorbent materials for removing heavy metals, as well as organic and inorganic contaminants from wastewater and soil, thus improving plant growth and production yield. Nowadays, the increasing technological applications of BBC and ABBC also include their employment as energy sources, to catalyze chemical reactions, to develop thermoelectrical devices, as 3D solar vapor-generation devices for water desalination, and as efficient photothermal-conversion devices. Anyway, although it has great potential as an alternative biomass to wood to produce BC, thus paving the way for new bio- and circular economy solutions, the study of bamboo-derived biomasses is still in its infancy. In this context, the main scope of this review was to support an increasing production of BBC and ABBC and to stimulate further studies about their possible applications, thus enlarging the current knowledge about these materials and allowing their more rational, safer, and optimized application. To this end, after having provided background concerning BC, its production methods, and its main applications, we have reviewed and discussed the main studies on BBC and ABBC and their applications reported in recent years. Biochar ; Bamboo ; Environmental science ; Context (archaeology) ; Biomass (ecology) ; Pulp and paper industry ; Renewable energy ; Waste management ; Pyrolysis ; Materials science ; Agronomy ; Engineering Supercapacitor Materials and Fabrication
Authors: Jun Luo | Omar Ben Said | Peigen Xie | Marco Gibaldi | Jake Burner | Cécile Pereira | Tom K. Woo
Source: npj Computational Materials (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call <i>MEPO-ML</i>, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs. Metal-organic framework ; Partial charge ; Graph ; Computer science ; Theoretical computer science ; Chemistry ; Charge (physics) ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Anastasiia Tukova | Nhung Thi Tuyet Nguyen | Alfonso E. Garcia‐Bennett | Alison Rodger | Yuling Wang
Source: Advanced Optical Materials (article, 2024-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic Nanostars: Unique Properties That Distinguish Them from Spherical Nanoparticles from a Biosensing Perspective Abstract Over the past three decades, plasmonic nanostructures, particularly spherical ones, have seen remarkable advancements. Recently, attention has shifted toward anisotropic nanoparticles, especially star‐shaped/branched structures such as plasmonic nanostars (PNS), due to their distinct properties. PNS offers superior electromagnetic enhancement effects, larger surface areas, and as well as non‐linear and unusual photothermal properties, setting them apart from spherical counterparts. Despite significant progress in synthetic methods and characterization of the particles, challenges remain in transitioning PNS technology into practical use. In this perspective article, the distinctive attributes of PNS in biosensing applications are discussed, beginning with an exploration of synthesis methodologies. Their optoelectronic properties are examined and discussed how these properties influence their interaction with different molecules from a biosensing perspective. With a focus on PNS, detailed insights are offered into their unique properties, current applications, and future potential. By fostering discussion and understanding of PNS development, this article aims to facilitate the translation of PNS technology into practical applications, encouraging targeted improvements and advancements. Materials science ; Plasmon ; Nanotechnology ; Biosensor ; Nanoparticle ; Perspective (graphical) ; Plasmonic nanoparticles ; Surface plasmon resonance ; Optoelectronics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Subhamay Pramanik | Bo Li | Darren M. Driscoll | Katherine R. Johnson | Barbara R. Evans | Joshua T. Damron | Alexander S. Ivanov | De‐en Jiang | Jeffrey D. Einkauf | Ilja Popovs | Santa Jansone‐Popova
Source: Journal of the American Chemical Society (article, 2024-08-13)
Field: Chemistry — Inorganic Chemistry

Tetradentate Ligand’s Chameleon-Like Behavior Offers Recognition of Specific Lanthanides The surging demand for high-purity individual lanthanides necessitates the development of novel and exceptionally selective separation strategies. At the heart of these separation systems is an organic compound that, based on its structural features, selectively recognizes the lighter or heavier lanthanides in the trivalent lanthanide (Ln) series. This work emphasizes the significant implications resulting from modifying the donor group configuration within an N,O-based tetradentate ligand and the changes in the solvation environment of Ln ions in the process of separating Lns, with the unique ability to achieve peak selectivity in the light, medium, and heavy Ln regions. The structural rigidity of the bis-lactam-1,10-phenanthroline ligand enforces size-based selectivity, displaying an exceptional affinity for Lns having larger ionic radii such as La. Modifying the ligand by eliminating one preorganization element (phenanthroline → bipyridine) results in the fast formation of complexes with light Lns, but, in the span of hours, the peak selectivity shifts toward middle Ln (Sm), resulting in time-resolved separation. As expected, at low nitric acid concentrations, the neutral tetradentate ligand complexes with Ln<sup>3+</sup> ions. However, the change in extraction mechanism is observed at high nitric acid concentrations, leading to the formation and preferential extraction of anionic heavy Ln species, [Ln(NO<sub>3</sub>)<sub><i>x</i>+3</sub>]<sup><i>x</i>-</sup>, that self-assemble with two ligands that have undergone protonation, forming intricate supramolecular architectures. The tetradentate ligand that is structurally balanced with restrictive and unrestrictive motifs demonstrates unique, controllable selectivity for light, middle, and heavy Lns, underscoring the pivotal role of solvation and ion interactions within the first and second coordination spheres. Chemistry ; Lanthanide ; Ligand (biochemistry) ; Computational biology ; Biochemistry ; Receptor ; Organic chemistry ; Ion Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes ; Polyoxometalates: Synthesis and Applications
Authors: Agustami Sitorus | Ravipat Lapcharoensuk
Source: Sensors (article, 2024-04-08)
Field: Chemistry — Analytical Chemistry

Exploring Deep Learning to Predict Coconut Milk Adulteration Using FT-NIR and Micro-NIR Spectroscopy Accurately identifying adulterants in agriculture and food products is associated with preventing food safety and commercial fraud activities. However, a rapid, accurate, and robust prediction model for adulteration detection is hard to achieve in practice. Therefore, this study aimed to explore deep-learning algorithms as an approach to accurately identify the level of adulterated coconut milk using two types of NIR spectrophotometer, including benchtop FT-NIR and portable Micro-NIR. Coconut milk adulteration samples came from deliberate adulteration with corn flour and tapioca starch in the 1 to 50% range. A total of four types of deep-learning algorithm architecture that were self-modified to a one-dimensional framework were developed and tested to the NIR dataset, including simple CNN, S-AlexNET, ResNET, and GoogleNET. The results confirmed the feasibility of deep-learning algorithms for predicting the degree of coconut milk adulteration by corn flour and tapioca starch using NIR spectra with reliable performance (<i>R</i><sup>2</sup> of 0.886-0.999, RMSE of 0.370-6.108%, and Bias of -0.176-1.481). Furthermore, the ratio of percent deviation (RPD) of all algorithms with all types of NIR spectrophotometers indicates an excellent capability for quantitative predictions for any application (RPD > 8.1) except for case predicting tapioca starch, using FT-NIR by ResNET (RPD < 3.0). This study demonstrated the feasibility of using deep-learning algorithms and NIR spectral data as a rapid, accurate, robust, and non-destructive way to evaluate coconut milk adulterants. Last but not least, Micro-NIR is more promising than FT-NIR in predicting coconut milk adulteration from solid adulterants, and it is portable for in situ measurements in the future. Starch ; Near-infrared spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Food science ; Mathematics ; Computer science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Coconut Research and Applications
Authors: Wout Bittremieux | Varun Ananth | William E. Fondrie | Carlo Melendez | Marina Pominova | Justin J. Sanders | Bo Wen | Melih Yilmaz | William Stafford Noble
Source: Mass Spectrometry Reviews (review, 2024-11-29)
Field: Chemistry — Spectroscopy

Deep Learning Methods for <i>De Novo</i> Peptide Sequencing Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing is required. Since the introduction of the DeepNovo algorithm in 2017, the field of de novo sequencing has been dominated by deep learning methods, which use large amounts of labeled mass spectrometry data to train multi-layer neural networks to translate from observed mass spectra to corresponding peptide sequences. Here, we describe these deep learning methods, outline procedures for evaluating their performance, and discuss the challenges in the field, both in terms of methods development and evaluation protocols. Chemistry ; Deep learning ; Computational biology ; Tandem mass spectrometry ; Mass spectrometry ; Genome ; DNA sequencing ; Field (mathematics) ; Artificial intelligence ; Computer science ; Chromatography ; Gene ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Elena Alexander | Kam W. Leong
Source: PNAS Nexus (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Nanodiamonds in biomedical research: Therapeutic applications and beyond Nanodiamonds (NDs) comprise a family of carbon-based nanomaterials (i.e. diameter <100 nm) with the same sp<sup>3</sup> lattice structure that gives natural diamonds their exceptional hardness and electrical insulating properties. Among all carbon nanomaterials-e.g. carbon nanotubes, nanodots, and fullerenes-NDs are of particular interest for biomedical applications because they offer high biocompatibility, stability in vivo, and a dynamic surface chemistry that can be manipulated to perform a seemingly limitless variety of ultra-specific tasks. NDs are already deepening our understanding of basic biological processes, while numerous laboratories continue studying these nanomaterials with an aim of making seismic improvements in the prevention, diagnosis, and treatment of human diseases. This review surveys approximately 2,000 the most recent articles published in the last 5 years and includes references to more than 150 of the most relevant publications on the biomedical applications of NDs. The findings are categorized by contemporary lines of investigation based on potential applications, namely: genetics and gene editing, drug delivery systems, neural interfacing, biomedical sensors, synthetic biology, and organ and tissue regeneration. This review also includes a brief background of NDs and the methods currently developed for their synthesis and preparation. Finally, recommendations for future investigations are offered. Nanotechnology ; Interfacing ; Biocompatibility ; Computer science ; Engineering ethics ; Materials science ; Engineering Diamond and Carbon-based Materials Research ; Graphene and Nanomaterials Applications ; Fullerene Chemistry and Applications
Authors: Giuseppe Bonifazi | Riccardo Gasbarrone | Roberta Palmieri | Silvia Serranti
Source: Waste and Biomass Valorization (article, 2024-02-08)
Field: Chemistry — Analytical Chemistry

A Characterization Approach for End-of-Life Textile Recovery Based on Short-Wave Infrared Spectroscopy Abstract Reusing and recycling End-Of-Life (EoL) textiles is a successful approach to develop sustainable and circular strategies in the apparel industry. Textile reuse and recycling can help to reduce the environmental impact of the fashion and textile industry by preserving natural resources and reducing waste. Textile fibers recognition and sorting, according to material composition, are of primary importance for the implementation of efficient and sustainable recycling strategies. In this work, Short-Wave InfraRed (SWIR: 1000–2500 nm) spectroscopy was applied to extract information regarding the fabric composition of different EoL textiles in order to set up a hierarchical classification procedure able to recognize different type of textile. In more detail, Partial Least Squares-Discriminant Analysis (PLS-DA) pattern recognition technique was used and classifications were performed in two steps: (1) recognition of the fiber origin [i.e. plant-derived, animal-derived, artificial textiles such as synthetic and/or Man-Made Cellulosic Fibers (MMCFs)] and, (2) discrimination of fabrics according to the material classes (i.e. silk, cotton, wool, viscose, linen, jute, polyester and blends). The proposed chemometric technique successfully classified textiles based on their spectral properties. The acquired results are highly promising and provide important insight into the EoL textile recycling business. These analytical techniques have the potential to be utilized to successfully automate the recycling process, either in addition to or as a replacement for manual processes, hence improving sorting procedures. Graphical Abstract Textile ; Reuse ; Wool ; Textile industry ; Process engineering ; Sorting ; Viscose ; Pulp and paper industry ; Computer science ; Waste management ; Engineering ; Biochemical engineering ; Pattern recognition (psychology) ; Materials science ; Artificial intelligence ; Composite material ; Algorithm Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Dyeing and Modifying Textile Fibers