jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. Radha | Zinb Makhlouf | Rasha Diab | Mohammad H. Al‐Sayah
Source: Royal Society Open Science (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Modifying cellulose fibres with carbon dots: a promising approach for the development of antimicrobial fibres This study focuses on the development of antimicrobial fibres for use in medical and healthcare textile industries. Carbon dots (CDs) were designed with boronic acid groups for the attachment to cellulose fibres found in cotton textiles and to enhance their attachment to glycogens on bacterial surfaces. Boronic acid-based and curcumin-based CDs were prepared and characterized using various techniques, showing a nanoscale size and zeta potential values. The CDs inhibited the growth of both <i>Staphylococcus epidermidis</i> and <i>Escherichia coli</i> bacteria, with UV-activated CDs demonstrating improved antibacterial activity. The antimicrobial activity of the CDs was then tested, revealing strong adherence to cellulose paper fibres with no CD diffusion and potent inhibition of bacterial growth. Cytotoxicity assays on human cell lines showed no toxicity towards cells at concentrations of up to 100 µg ml<sup>-1</sup> but exhibited increased toxicity at concentrations exceeding 1000 µg ml<sup>-1</sup>. However, CD-modified cellulose paper fibres showed no toxicity against human cell lines, highlighting the antimicrobial properties of the CD-modified cellulose fibres are safe for human use. These findings show promising potential for applications in both industrial and clinical settings. Antimicrobial ; Cellulose ; Bacterial cellulose ; Staphylococcus epidermidis ; Escherichia coli ; Chemistry ; Toxicity ; Boronic acid ; Cytotoxicity ; Bacteria ; Antibacterial activity ; Nanotechnology ; Materials science ; Nuclear chemistry ; Combinatorial chemistry ; Biochemistry ; Staphylococcus aureus ; Organic chemistry ; Biology ; In vitro Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Yuri Cho | Rubén Laplaza | Sergi Vela | Clémence Corminbœuf
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automated prediction of ground state spin for transition metal complexes Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the <i>cell2mol</i> software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with <i>cell2mol</i>, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes. Ground state ; Spin (aerodynamics) ; Computation ; Spin states ; Complement (music) ; Charge (physics) ; Transition metal ; Quantum chemical ; State (computer science) ; Electronic structure ; Chemical physics ; Materials science ; Chemistry ; Molecule ; Crystallography ; Physics ; Computational chemistry ; Condensed matter physics ; Computer science ; Atomic physics ; Thermodynamics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Huanhuan Cai | Aixue Li | Fu Qi | Rongmei Liu | Xiaomeng Tang | Dan Li | Yongwei Gu | Jiyong Liu
Source: Materials Advances (article, 2024-01-01)
Field: Materials Science — Biomaterials

Application of biodegradable microsphere injections: an anticancer perspective Carrier materials for biodegradable microsphere injections and their application in cancer. Microsphere ; Perspective (graphical) ; Pharmacology ; Biomedical engineering ; Medicine ; Computer science ; Engineering ; Chemical engineering Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Cancer Treatment and Pharmacology
Authors: Andrey A. Buglak | Minh Tho Nguyen
Source: Biophysical Reviews (review, 2024-06-07)
Field: Materials Science — Materials Chemistry

Interactions of coinage metal nanoclusters with low-molecular-weight biocompounds Nanoclusters ; Biomolecule ; Nanotechnology ; Chemistry ; Biopolymer ; Metal ions in aqueous solution ; Biocompatibility ; Fluorescence ; Metal ; Nanomaterials ; Template ; Combinatorial chemistry ; Materials science ; Polymer ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Ana Carolina Ferreira de Albuquerque | Lucas H. Martorano | Fernando Martins dos Santos
Source: Frontiers in Natural Products (article, 2024-01-03)
Field: Chemistry — Spectroscopy

Are we still chasing molecules that were never there? The role of quantum chemical simulations of NMR parameters in structural reassignment of natural products Covering: 2019 to 2023. Even with the advent of modern and complementary spectroscopy techniques, comprehensive characterization of natural product continues to represent an onerous and time-consuming task, being far away to become rather “routine”. Mainly due to their highly complex structures and small amount of isolated sample, in milligram or sub-milligram quantities, structural misassignment of natural products are still a recurrence theme in the modern literature. Since the seminal paper from Nicolau and Snider, in 2005, evaluating the various cases of reassignment of natural products, from the present era, in which NMR parameters calculations play such an important role in the structural elucidation of natural products, helping to uncover and ultimately revise the structure of previously reported compounds, a pertinent question arises: are we still chasing molecules that were never there? In this minireview, we intent to discuss the current state of computational NMR parameter calculations, with a particular focus on their application in the structural determination of natural products. Additionally, we have conducted a comprehensive survey of the literature spanning the years 2019–2023, in order to select and discuss recent noteworthy cases of incorrectly assigned structures that were revised through NMR calculations. Therefore, our main goal is to show what can be done through computational simulations of NMR parameters, currently user-friendly and easily implemented by non-expert users with basic skills in computational chemistry, before venturing into complex and time-consuming total synthesis projects. In conclusion, we anticipate a promising future for NMR parameter calculations, fueled by the ongoing development of user-friendly tools and the integration of artificial intelligence. The emergence of these advancements is poised to broaden the applications of NMR simulations, offering a more accessible and reliable means to address the persistent challenge of structural misassignments in natural product chemistry. Natural (archaeology) ; Biochemical engineering ; Computer science ; Theme (computing) ; Natural product ; Task (project management) ; Chemistry ; Computational chemistry ; Engineering ; Systems engineering ; Organic chemistry ; Geography ; World Wide Web Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Ying Zhou | Hongyan Li | Eric Tse | Hongzhe Sun
Source: Chemical Science (article, 2024-01-01)
Field: Chemistry — Electrochemistry

Metal-detection based techniques and their applications in metallobiology Metals are essential for human health and play a crucial role in numerous biological processes and pathways. Gaining a deeper insight into these biological events will facilitate novel strategies for disease prevention, early detection, and personalized treatment. In recent years, there has been significant progress in the development of metal-detection based techniques from single cell metallome and proteome profiling to multiplex imaging, which greatly enhance our comprehension of the intricate roles played by metals in complex biological systems. This perspective summarizes the recent progress in advanced metal-detection based techniques and highlights successful applications in elucidating the roles of metals in biology and medicine. Technologies including machine learning that couple with single-cell analysis such as mass cytometry and their application in metallobiology, cancer biology and immunology are also emphasized. Finally, we provide insights into future prospects and challenges involved in metal-detection based techniques, with the aim of inspiring further methodological advancements and applications that are accessible to chemists, biologists, and clinicians. Nanotechnology ; Perspective (graphical) ; Computer science ; Data science ; Biochemical engineering ; Computational biology ; Engineering ; Biology ; Materials science ; Artificial intelligence Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Trace Elements in Health
Authors: Kabir Garba | Habu Iyodo Mohammed | Yusuf Makarfi Isa
Source: Heliyon (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Pyrolysis of metal oxides treated Canarium schweinfurthii Shell: Investigation of thermogravimetric kinetics and thermodynamics Metal oxides as catalysts alter the properties of the pyrolysis vapor secondary reactions during the thermal decomposition of several biomass leading to high-value bio-oils. This study aimed to investigate the thermal decomposition characteristics of <i>Canarium Schweinfurthii</i> (CS) shells that were treated with various metal oxides (ZnO, CuO, Fe<sub>2</sub>O<sub>3</sub>/FeO, and Fe<sub>2</sub>O<sub>3</sub>) using pyrolysis. The study also sought to identify pyrolysis reaction parameters (kinetics and thermodynamics parameters) that are not widely documented. Thermogravimetric pyrolysis was carried out at different heating rates, and the undocumented pyrolysis kinetic parameters were determined using the Flynn-Wall Ozawa method (FWO) according to American Standard Testing and Materials (ASTM) 6441 guidelines for assessing biomass decomposition. The metal oxide-treated CS shells lost significant weight between 62 and 67 wt% during the thermogravimetric pyrolysis, lower than 75 wt% of the CS shell. The average activation energies (E<sub>α</sub>) for pyrolysis of the ZnO, CuO, Fe<sub>2</sub>O<sub>3</sub>/FeO, and Fe<sub>2</sub>O<sub>3</sub> treated CS shells were 203.04, 155.35, 338.85, and 219.92 kJ/mol, respectively in contrast to that of the untreated CS-shell. The Bayesian Information Criteria revealed that the diffusion kinetics of the Gistling-Brounshtein model best describes the pyrolysis of the shell mixed with metal oxides. The metal oxides affected the CS shells' pyrolysis kinetic parameter (E<sub>α</sub>), which can promote pyrolysis vapor upgrading to encourage the widespread use of metal oxides in pyrolysis for bioenergy and chemical recovery. Thermogravimetric analysis ; Kinetics ; Pyrolysis ; Thermodynamics ; Metal ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Physics ; Engineering Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Polymer Nanocomposites and Properties
Authors: Natsuo Okada | Hiromasa Nozaki | Shinichiro Nakamura | Elsa Pansilvania Andre Manjate | Angesom Gebretsadik | Yoko Ohtomo | Takahiko Arima | Youhei Kawamura
Source: Scientific Reports (article, 2024-05-21)
Field: Chemistry — Analytical Chemistry

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities. Sorting ; Component (thermodynamics) ; Mineral ; Selection (genetic algorithm) ; Arsenic ; Component analysis ; Computer science ; Environmental science ; Materials science ; Artificial intelligence ; Metallurgy ; Algorithm ; Physics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Mineral Processing and Grinding
Authors: Hyo In Yoon | Su Hyeon Lee | Da Hye Ryu | Hye Lim Choi | Soo Hyun Park | Je Hyeong Jung | Ho‐Youn Kim | Jung‐Seok Yang
Source: Frontiers in Plant Science (article, 2024-04-26)
Field: Chemistry — Analytical Chemistry

Non-destructive assessment of cannabis quality during drying process using hyperspectral imaging and machine learning <i>Cannabis sativa</i> L. is an industrially valuable plant known for its cannabinoids, such as cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), renowned for its therapeutic and psychoactive properties. Despite its significance, the cannabis industry has encountered difficulties in guaranteeing consistent product quality throughout the drying process. Hyperspectral imaging (HSI), combined with advanced machine learning technology, has been used to predict phytochemicals that presents a promising solution for maintaining cannabis quality control. We examined the dynamic changes in cannabinoid compositions under diverse drying conditions and developed a non-destructive method to appraise the quality of cannabis flowers using HSI and machine learning. Even when the relative weight and water content remained constant throughout the drying process, drying conditions significantly influenced the levels of CBD, THC, and their precursors. These results emphasize the importance of determining the exact drying endpoint. To develop HSI-based models for predicting cannabis quality indicators, including dryness, precursor conversion of CBD and THC, and CBD : THC ratio, we employed various spectral preprocessing methods and machine learning algorithms, including logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB). The LR model demonstrated the highest accuracy at 94.7-99.7% when used in conjunction with spectral pre-processing techniques such as multiplicative scatter correction (MSC) or Savitzky-Golay filter. We propose that the HSI-based model holds the potential to serve as a valuable tool for monitoring cannabinoid composition and determining optimal drying endpoint. This tool offers the means to achieve uniform cannabis quality and optimize the drying process in the industry. Hyperspectral imaging ; Quality (philosophy) ; Process (computing) ; Computer science ; Artificial intelligence ; Environmental science ; Remote sensing ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Rweetuparna Guha | Sami Malola | Malak Rafik | Maya Khatun | Anna Gonzàlez‐Rosell | Hannu Häkkinen | Stacy M. Copp
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Fragmentation patterns of DNA-stabilized silver nanoclusters under mass spectrometry DNA-stabilized silver nanoclusters (Ag<sub>N</sub>-DNAs) are emitters with tuneable structures and photophysical properties. While understanding of the sequence-structure-property relationships of Ag<sub>N</sub>-DNAs has advanced significantly, their chemical transformations and degradation pathways are far less understood. To advance understanding of these pathways, we analysed the fragmentation products of 21 different red and NIR Ag<sub>N</sub>-DNAs using negative ion mode electrospray ionization mass spectrometry (ESI-MS). Ag<sub>N</sub>-DNAs were found to lose Ag<sup>+</sup> under ESI-MS conditions, and sufficient loss of silver atoms can lead to a transition to a lesser number of effective valence electrons, <i>N</i><sub>0</sub>. Of more than 400 mass spectral peaks analysed, only even values of <i>N</i><sub>0</sub> were identified, suggesting that solution-phase Ag<sub>N</sub>-DNAs with odd values of <i>N</i><sub>0</sub> are unlikely to be stable. Ag<sub>N</sub>-DNAs stabilized by three DNA strands were found to fragment significantly more than Ag<sub>N</sub>-DNAs stabilized by two DNA strands. Moreover, the fragmentation behaviour depends strongly on the DNA template sequence, with diverse fragmentation patterns even for Ag<sub>N</sub>-DNAs with similar molecular formulae. Molecular dynamics simulations, with forces calculated from density functional theory, of the fragmentation of (DNA)<sub>2</sub>(Ag<sub>16</sub>Cl<sub>2</sub>)<sup>8+</sup> with a known crystal structure show that the 6-electron Ag<sub>16</sub>Cl<sub>2</sub> core fragments into a 4-electron Ag<sub>10</sub> and a 2-electron Ag<sub>6</sub>, preserving electron-pairing rules even at early stages of the fragmentation process, in agreement with experimental observation. These findings provide new insights into the mechanisms by which Ag<sub>N</sub>-DNAs degrade and transform, with relevance for their applications in sensing and biomedical applications. Nanoclusters ; Fragmentation (computing) ; Mass spectrometry ; DNA ; Chemistry ; Materials science ; Nanotechnology ; Chemical physics ; Computer science ; Chromatography ; Biochemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Xinlei Wei | Xue Yang | Congcong Hu | Qiangzi Li | Qianqian Liu | Yue Wu | Leipeng Xie | Xiao Ning | Fei Li | Tao Cai | Zhiguang Zhu | Y.‐H. Percival Zhang | Yanfei Zhang | Xuejun Chen | Chun You
Source: Nature Communications (article, 2024-04-16)
Field: Materials Science — Biomaterials

ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA In vitro biotransformation (ivBT) facilitated by in vitro synthetic enzymatic biosystems (ivSEBs) has emerged as a highly promising biosynthetic platform. Several ivSEBs have been constructed to produce poly-3-hydroxybutyrate (PHB) via acetyl-coenzyme A (acetyl-CoA). However, some systems are hindered by their reliance on costly ATP, limiting their practicality. This study presents the design of an ATP-free ivSEB for one-pot PHB biosynthesis via acetyl-CoA utilizing starch-derived maltodextrin as the sole substrate. Stoichiometric analysis indicates this ivSEB can self-maintain NADP<sup>+</sup>/NADPH balance and achieve a theoretical molar yield of 133.3%. Leveraging simple one-pot reactions, our ivSEBs achieved a near-theoretical molar yield of 125.5%, the highest PHB titer (208.3 mM, approximately 17.9 g/L) and the fastest PHB production rate (9.4 mM/h, approximately 0.8 g/L/h) among all the reported ivSEBs to date, and demonstrated easy scalability. This study unveils the promising potential of ivBT for the industrial-scale production of PHB and other acetyl-CoA-derived chemicals from starch. Maltodextrin ; Biotransformation ; In vitro ; Starch ; Chemistry ; Biochemistry ; Food science ; Enzyme ; Organic chemistry biodegradable polymer synthesis and properties ; Microbial Metabolic Engineering and Bioproduction ; Biofuel production and bioconversion