Hasil untuk "Inorganic chemistry"

M. T. Pope, A. Müller

Haowei Hua, Chen Liang, Ding Pan et al.

Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $α_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.

Shekufeh Alaei, Khosro Mohammadi, Payam Hayati et al.

A novel one-dimensional mercury coordination polymer (CP), identified as [(μ2-Cl)(Ina)Hg(μ3-Cl)Hg(μ2-Cl)2(Ina)]n (1) (where Ina represents isonicotinic acid or 4-pyridinecarboxylic acid), was synthesized via the interaction of isonicotinic acid with mercury(II) salt. This synthesis was achieved through two distinct experimental approaches: layering methods for the formation of single crystals (1) and sonochemical irradiation for the production of nanostructures (1′). The structural characterization of (1) was performed using X-ray diffraction and crystallography techniques. Further characterization involved a range of methods, including X-ray powder diffraction (XRD), infrared (IR) spectroscopy, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Hirshfeld surface analysis (HSA). The CP of (1) features two types of metal centers, exhibiting coordination numbers of 5 and 6. In this structure, each mercury atom is coordinated to chlorine, nitrogen, and oxygen atoms derived from the ligands. Additionally, antibacterial properties were tested on seven Gram-positive bacteria and nine Gram-negative bacteria. Anticancer properties were tested on both OCAR3 (cancer) and VERO (normal) cells; as a result, the antibacterial and anticancer activities of nanoparticle [(μ2-Cl)(Ina)Hg(μ3-Cl)Hg(μ2-Cl)2(Ina)]n (1′) were evaluated, revealing that the antibacterial efficacy of the nanoparticles was comparable to that of standard antibiotics. The anticancer properties were effective in destroying cancer cells while preserving the integrity of normal cells. Consequently, both antibacterial and anticancer properties demonstrated promising results.

Trond Saue

In this mini-review I look into the physics underlying the theory of electronic structure of atoms and molecules. Quantum mechanics is needed to understand the structure of the periodic table. Special relativity is indispensable for a correct description of the chemistry of the heavy elements. With increased accuracy of quantum chemical calculations, it is natural to ask if chemistry needs more physics.

Joana Ribeiro, Henrique Araújo-Silva, Mário Fernandes et al.

Abstract According to The World Alzheimer Report 2023 by Alzheimer’s Disease International (ADI) estimates that 33 to 38.5 million people worldwide suffer from Alzheimer’s Disease (AD). A crucial hallmark associated with this disease is associated with the deficiency of the brain neurotransmitter acetylcholine, due to an affected acetylcholinesterase (AChE) activity. Marine organisms synthesize several classes of compounds, some of which exhibit significant AChE inhibition, such as petrosamine, a coloured pyridoacridine alkaloid. The aim of this work was to characterize the activity of petrosamine isolated for the first time from a Brazilian marine sponge, using two neurotoxicity models with aluminium chloride, as exposure to aluminium is associated with the development of neurodegenerative diseases. The in vitro model was based in a neuroblastoma cell line and the in vivo model exploited the potential of zebrafish (Danio rerio) embryos in mimicking hallmarks of AD. To our knowledge, this is the first report on petrosamine’s activity over these parameters, either in vitro or in vivo, in order to characterize its full potential for tackling neurotoxicity. Graphical Abstract

Vaishali Gupta, Satyendra Singh

Advanced oxidation processes have attracted considerable attention for wastewater treatment, air purification, CO2 reduction and many more pollution control applications. Environmentally friendly (K0.5Na0.5)NbO3 (abbreviated as 'KNN') is emerging as a lead-free photocatalyst due to its good piezoelectric response and high Curie temperature. In this work, KNN photocatalysts were synthesized by two methods i.e. solid-state and sol-gel routes and abbreviated as KNN-SS and KNN-SG, respectively. X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) confirmed the orthorhombic structure for both the samples. Morphological studies were done using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Average particle size was estimated using ImageJ software which is to be around 1.38 ​μm and 278 ​nm for KNN-SS and KNN-SG samples respectively. Zeta potential measurements estimated the average surface charge on the particles i.e. 67.099 ​mV for KNN-SS and 69.115 ​mV for KNN-SG samples. Tauc’ plot was employed to find the optical bandgap, which was estimated around 3 ​eV for both the samples. Photoluminescence (PL) spectroscopy confirmed that KNN-SG sample has a lower recombination rate than KNN-SS sample as it exhibited lower emission intensity. Better photocatalytic result of 92.8 ​% degradation of methylene blue dye in just 80 ​min has been observed for KNN-SG sample, indicating smaller particle size causes delayed recombination, which enhances higher photodegradation of the material. Lead-free ferroelectric KNN samples with smaller particle sizes can be a promising candidate for these effluents.

Paulina Czechowicz, Magdalena Gebert, Sylwia Bartoszewska et al.

Abstract Regulation of endoplasmic reticulum (ER) homeostasis plays a critical role in maintaining cell survival. When ER stress occurs, a network of three pathways called the unfolded protein response (UPR) is activated to reestablish homeostasis. While it is known that there is cross-talk between these pathways, how this complex network is regulated is not entirely clear. Using human cancer and non-cancer cell lines, two different genome-wide approaches, and two different ER stress models, we searched for miRNAs that were decreased during the UPR and surprisingly found only one, miR-1244, that was found under all these conditions. We also verified that ER-stress related downregulation of miR-1244 expression occurred with 5 different ER stressors and was confirmed in another human cell line (HeLa S3). These analyses demonstrated that the outcome of this reduction during ER stress supported both IRE1 signaling and elevated BIP expression. Further analysis using inhibitors specific for IRE1, ATF6, and PERK also revealed that this miRNA is impacted by all three pathways of the UPR. This is the first example of a complex mechanism by which this miRNA serves as a regulatory check point for all 3 pathways that is switched off during UPR activation. In summary, the results indicate that ER stress reduction of miR-1244 expression contributes to the pro-survival arm of UPR.

G. Tuli, S. Prakash, Soumen Basu

Braden M. Weight, Xinyang Li, Yu Zhang

Light-matter interaction not only plays an instrumental role in characterizing materials' properties via various spectroscopic techniques but also provides a general strategy to manipulate material properties via the design of novel nanostructures. This perspective summarizes recent theoretical advances in modeling light-matter interactions in chemistry, mainly focusing on plasmon and polariton chemistry. The former utilizes the highly localized photon, plasmonic hot electrons, and local heat to drive chemical reactions. In contrast, polariton chemistry modifies the potential energy curvatures of bare electronic systems, and hence their chemistry, via forming light-matter hybrid states, so-called polaritons. The perspective starts with the basic background of light-matter interactions, molecular quantum electrodynamics theory, and the challenges of modeling light-matter interactions in chemistry. Then, the recent advances in modeling plasmon and polariton chemistry are described, and future directions toward multiscale simulations of light-matter interaction-mediated chemistry are discussed.

Claudio Zeni, Robert Pinsler, Daniel Zügner et al.

The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating entirely novel materials given desired property constraints. Despite recent progress, current generative models have low success rate in proposing stable crystals, or can only satisfy a very limited set of property constraints. Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, we introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset. Compared to prior generative models, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 15 times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, novel materials with desired chemistry, symmetry, as well as mechanical, electronic and magnetic properties. Finally, we demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk. We believe that the quality of generated materials and the breadth of MatterGen's capabilities represent a major advancement towards creating a universal generative model for materials design.

Siru Ouyang, Zhuosheng Zhang, Bing Yan et al.

Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in the field of chemistry. Different from the simple chemistry tasks (e.g., molecule classification) addressed in previous studies, complex chemistry problems require not only vast knowledge and precise calculation, but also compositional reasoning about rich dynamic interactions of different concepts (e.g., temperature changes). Our study shows that even advanced LLMs, like GPT-4, can fail easily in different ways. Interestingly, the errors often stem not from a lack of domain knowledge within the LLMs, but rather from the absence of an effective reasoning structure that guides the LLMs to elicit the right knowledge, incorporate the knowledge in step-by-step reasoning, and iteratively refine results for further improved quality. On this basis, we introduce StructChem, a simple yet effective prompting strategy that offers the desired guidance and substantially boosts the LLMs' chemical reasoning capability. Testing across four chemistry areas -- quantum chemistry, mechanics, physical chemistry, and kinetics -- StructChem substantially enhances GPT-4's performance, with up to 30\% peak improvement. Our analysis also underscores the unique difficulties of precise grounded reasoning in science with LLMs, highlighting a need for more research in this area. Code is available at \url{https://github.com/ozyyshr/StructChem}.

Jiadong Chen, Samuel R. Cross, Lincoln J. Miara et al.

Efficient synthesis recipes are needed both to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Oftentimes the solid-state synthesis of multicomponent oxides is impeded by undesired byproduct phases, which can kinetically trap reactions in an incomplete non-equilibrium state. We present a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy competing byproducts, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides with chemistries representative of intercalation battery cathodes and solid-state electrolytes, we perform 224 reactions spanning 27 elements with 28 unique precursors. Our predicted precursors frequently yield target materials with higher phase purity than when starting from traditional precursors. Robotic laboratories offer an exciting new platform for data-driven experimental science, from which we can develop new insights into materials synthesis for both robot and human chemists.

Joel Martínez, Maricarmen Hernández-Rodríguez, Abraham Méndez-Albores et al.

Aflatoxin B₁ (AFB₁) exhibits the most potent mutagenic and carcinogenic activity among aflatoxins. For this reason, AFB₁ is recognized as a human group 1 carcinogen by the International Agency of Research on Cancer. Consequently, it is essential to determine its properties and behavior in different chemical systems. The chemical properties of AFB₁ can be explored using computational chemistry, which has been employed complementarily to experimental investigations. The present review includes in silico studies (semiempirical, Hartree–Fock, DFT, molecular docking, and molecular dynamics) conducted from the first computational study in 1974 to the present (2022). This work was performed, considering the following groups: (a) molecular properties of AFB₁ (structural, energy, solvent effects, ground and the excited state, atomic charges, among others); (b) theoretical investigations of AFB₁ (degradation, quantification, reactivity, among others); (c) molecular interactions with inorganic compounds (Ag⁺, Zn²⁺, and Mg²⁺); (d) molecular interactions with environmentally compounds (clays); and (e) molecular interactions with biological compounds (DNA, enzymes, cyclodextrins, glucans, among others). Accordingly, in this work, we provide to the stakeholder the knowledge of toxicity of types of AFB₁-derivatives, the structure–activity relationships manifested by the bonds between AFB₁ and DNA or proteins, and the types of strategies that have been employed to quantify, detect, and eliminate the AFB₁ molecule.

Shuangyang Li, Qixuan Yu, Hongpeng Li et al.

Regenerative medicine is a complex discipline that is becoming a hot research topic. Skin, bone, and nerve regeneration dominate current treatments in regenerative medicine. A new type of drug is urgently needed for their treatment due to their high vulnerability to damage and weak self-repairing ability. A self-assembled peptide hydrogel is a good scaffolding material in regenerative medicine because it is similar to the cytoplasmic matrix environment; it promotes cell adhesion, migration, proliferation, and division; and its degradation products are natural and harmless proteins. However, fewer studies have examined the specific mechanisms of self-assembled peptide hydrogels in promoting tissue regeneration. This review summarizes the applications and mechanisms of self-assembled short peptide and peptide hydrogels in skin, bone, and neural healing to improve their applications in tissue healing and regeneration.

P. Baby Shakila, Abdurahman Hajinur Hirad, Abdullah A. Alarfaj et al.

Multiple chemodrugs with nanotechnology have proven to be an effective cancer treatment technique. When taken combined, cabazitaxel (CTX) and cisplatin (PT) have more excellent cytotoxic effects than drugs used alone in the chemotherapy of several different cancers. However, several severe side effects are associated with using these chemotherapy drugs in cancer patients. Gold nanomaterials (AuNMs) are promising as drug carriers because of their small diameter, easy surface modifications, good biocompatibility, and strong cell penetration. This work aimed to determine the CTX and PT encapsulated with AuNMs against human glioma U87 cancer cells. The fabrication of the AuNMs achieved a negative surface charge, polydispersity index, and the mean sizes. The combined cytotoxic effect of CTX and PT bound to AuNMs was greater than that of either drug alone when tested on U87 cells. The half inhibitory concentration (IC50) values for free PT were 54.7 μg/mL (at 24 h) and 4.8 g μg/mL (at 72 h). Results acquired from the MTT assay show cell growth decreases time- and concentration-dependent AuNMs, free CTX, free PT, and AuNMs@CTX/PT-induced cytotoxicity and, ultimately, the cell death of U87 cells via apoptosis. The biochemical apoptosis staining techniques investigated the cells’ morphological changes of the cells (acridine orange and ethidium bromide (AO-EB) and nuclear staining (DAPI) techniques). The AO-EB and nuclear staining results reveal that the NPs effectively killed cancer cells. Furthermore, the flow cytometry analysis examined the mode of cell death. Therefore, AuNMs@CTX/PT has excellent potential in the cancer therapy of different cancer cells.

Mariana Chelu, Adina Magdalena Musuc, Monica Popa et al.

<i>Aloe vera</i>-based hydrogels have emerged as promising platforms for the delivery of therapeutic agents in wound dressings due to their biocompatibility and unique wound-healing properties. The present study provides a comprehensive overview of recent advances in the application of <i>Aloe vera</i>-based hydrogels for wound healing. The synthesis methods, structural characteristics, and properties of <i>Aloe vera</i>-based hydrogels are discussed. Mechanisms of therapeutic agents released from <i>Aloe vera</i>-based hydrogels, including diffusion, swelling, and degradation, are also analyzed. In addition, the therapeutic effects of <i>Aloe vera</i>-based hydrogels on wound healing, as well as the reduction of inflammation, antimicrobial activity, and tissue regeneration, are highlighted. The incorporation of various therapeutic agents, such as antimicrobial and anti-inflammatory ones, into <i>Aloe vera</i>-based hydrogels is reviewed in detail. Furthermore, challenges and future prospects of <i>Aloe vera</i>-based hydrogels for wound dressing applications are considered. This review provides valuable information on the current status of <i>Aloe vera</i>-based hydrogels for the delivery of therapeutic agents in wound dressings and highlights their potential to improve wound healing outcomes.