Analytical Chemistry
DOI: 10.1021/acs.analchem.3c02873
[ASAP] Interfacial Profiling of MicroRNAs at Patterned Nanogaps for an Integrated Microfluidic-SERS Liquid Biopsy
Analytical Chemistry
DOI: 10.1021/acs.analchem.3c02945
Molybdenum‐based Nanocatalysts for CO Oxidation Reactions in Direct Alcohol Fuel Cells: A Critical Review
Carbon monoxide (CO) oxidation is crucial in fuel cell anodes. Recent research has focused on electrocatalysts that synergistically enhance CO oxidation alongside alcohol/hydrogen oxidation. High sensitivity and selectivity for CO oxidation at lower onset potentials are the key objectives. Molybdenum (Mo) has emerged as a promising non-noble transition metal co-catalyst for CO oxidation. Mo versatility arises from its ability to alloy with Pt and mix with other non-noble transition metals in various forms (MoOx, MoS2, MoC). Carbon-supported Mo nanoparticles have shown potential in reducing Pt loading and improving CO tolerance due to Mo oxyphilic properties, effectively oxidizing weakly adsorbed CO and lowering onset and peak potentials. However, challenges persist, such as a limited potential window for CO oxidation and decreased CO adsorption affinity at higher potentials. Addressing these issues requires understanding factors affecting Mo-based electrocatalysts (Mo-ECs) activity, including PtMo alloy composition, Mo's chemical state, cell temperature, and the role of carbon support. This article provides a comprehensive review of Mo-ECs role in CO electrooxidation in direct alcohol fuel cells (DAFCs) over the past two decades.
Transition Metal‐Catalyzed Migratory Tsuji‐Trost Reaction
Beyond the scope of classical Tsuji-Trost reaction, an alkene bearing a remote leaving group is not considered as a potential substrate for allylation. The concept of migratory Tsuji-Trost reaction focuses on the transformations with this type of substrates. Two pathways including olefin migration and leaving group migration to form typical allyl intermediate for following substitution have been explored to demonstrate the feasibility of this new concept.
Synthesis, Structure and Magnetic Behavior of a Novel Series of Trinuclear Windwheel Complexes
Three trinuclear windwheel-shaped complexes were synthesized using M(II) (M = Mn, Co, Ni) ions, to give the easy-axis and easy-plane magnetic anisotropy, and the Co(II) complexes exhibited slow magnetic relaxation behavior.
Comprehensive Summary
A new family of trinuclear windwheel complexes with molecular formula [MII 3(tpa)3(μ-ttc)](ClO4)3·n(sol) (ttc = 1,3,5-triazine-2,4,6-trithiol; tpa = tris(2-pyridylmethyl)amine; M = Mn, n = 2, sol = CH3CN, 1; M = Co, n = 1, sol = CH3CN, 2; M = Ni, n = 0, 3) were synthesized and characterized. Single-crystal X-ray diffraction revealed that three metal centers in 1—3 are connected by ttc bridge, forming a regular triangular MII 3 core. Each metal center is bonded by chelating S, N atoms from ttc and by N atoms from tpa. Magnetic studies showed that 1—3 displayed antiferromagnetic behavior and further gave the easy-axis anisotropy (D = −0.77 cm−1 for 1 and −8.13 cm−1 for 2) and easy-plane anisotropy (D = 5.08 cm−1 for 3). Moreover, 2 exhibited field-induced slow magnetic relaxation behavior and their effective energy barriers were roughly evaluated U eff = 6.9 K.
Yellow‐Fluorescence Carbon Dots Employed for pH Sensing and Detection of Tigecycline†
Here, we proposed one type of CDs doped with nitrogen and sulfur through the hydrothermal method, which exhibited the obvious yellow-fluorescence in aqueous solution. Importantly, the fluorescence intensity of CDs decreased with pH decreasing in the acidic range, thus exhibiting the potential of pH sensing. Importantly, introducing tigecycline into these CDs resulted in their decreased fluorescence, thus we further established a strategy of detecting tigecycline.
Comprehensive Summary
Long-wavelength fluorescence carbon dots (CDs) show great importance in multiple fields, especially for the biochemical sensing. Here, we proposed one type of CDs doped with nitrogen and sulfur through the hydrothermal method, which exhibited obvious yellow-fluorescence in aqueous solution. Importantly, the fluorescence intensity of CDs decreased with pH decreasing in the acidic range, thus a linear relationship between pH and fluorescence intensity was established, exhibiting the potential of pH sensing. Additionally, introducing tigecycline into CDs resulted in their decreased fluorescence, thus, we further established a strategy of detecting tigecycline with the concentration range of 200 μM to 7 nM. Meanwhile, we elucidated the static quenching as the major mechanism for CDs responding tigecycline, which was induced by the formed new complex between CDs and tigecycline. Furthermore, the practicality of the method was verified by examining the recovery of tigecycline in the actual lake-water samples.
CO2‐Free Calcium Carbide Manufacturing: Demanded Strategy in the Carbon‐Neutral Chemical Industry
Modern methods for producing CaC2 are associated with a large amount of CO2 emissions and energy costs. The use of metallic Ca as a starting reagent can affect both the complete removal of CO2 emissions and the reduction of energy costs due to a lower reaction temperature.
Comprehensive Summary
Calcium carbide is considered a possible key component in the sustainable carbon cycle, including convenient recycling of carbon wastes to industrial uptake. However, currently employed CaC2 manufacturing process produces significant amounts of CO2. One of the main factors of its appearance is the formation of carbon oxide during the reaction. The reaction of lime ore with coal inevitably results in the formation of CO and the loss of one carbon atom. CO is usually burnt, forming CO2 to maintain the required high temperature during synthesis – 2200 °C. In the present study, we discuss that the use of calcium metal instead of lime represents a good opportunity to prevent CO2 emission since the reaction of Ca with carbon occurs in an atom-efficient manner and results in only CaC2 at a much lower temperature of 1100 °C. Here, the reaction of Ca with carbon was successfully tested to synthesize CaC2. The desired product was isolated in gram-scale amounts in 97.2% yield and 99% purity. The environmental friendliness of the proposed method originates from the calculations of the E-factor. Rationalization is provided concerning the cost factor of Ca within the considered process.
Balancing and Therapeutic Roles of CXCR4‐Inhibiting Nanomedicine via Synergetic Regulation of Hepatic Stellate Cells and Extracellular Matrix in Liver Injury†
A new CXCR4-inhibiting nanomedicine PAMD/Zn@siPAI-1 was designed to synergistically regulate the hepatic stellate cells (HSCs) activation and extracellular matrix (ECM) deposition.
Comprehensive Summary
Inflammation is associated with different stages of liver disease, including acute injury, fibrosis, cirrhosis, and hepatoma. During the progression of liver inflammation, activation of hepatic stellate cells (HSCs) and extracellular matrix (ECM) deposition are critical pathologies, and thus the combined therapy using HSCs and ECM as targets represents a promising strategy in the treatment of liver injury. Here, a novel CXCR4-inhibiting nanomedicine that can simultaneously deliver AMD3100 (CXCR4 antagonist) and siPAI-1 (siRNA of plasminogen activator inhibitor-1) was designed and developed to reverse liver fibrosis by inhibiting HSCs activation and degrading ECM deposition. With this goal in mind, a Zn(II) coordinated polymeric AMD3100 named PAMD/Zn polymer with siRNA delivery and CXCR4 antagonism capabilities was synthesized. Overall, our results suggest that PAMD/Zn recruits pro-inflammatory cells for fibrogenesis and inhibits the activation of HSCs for fibrolysis at various stages of liver injury. Its use in conjunction with PAI-1 silencing achieved satisfactory therapeutic efficacy in liver injury and fibrosis. The derivative CXCR4-inhibiting nanomedicine is a versatile platform that offers valuable benefits for the treatment of liver diseases.
Novel pyrazole‐based benzofuran derivatives as anticancer agents: Synthesis, biological evaluation and molecular docking investigations
In this work, the design, synthesis and mechanistic studies of a novel pyrazole-based benzofuran derivatives 1-8 as anticancer agents were discussed. Cytotoxic potency of the title compounds was evaluated against the lung carcinoma A-549, human-derived colorectal adenocarcinoma HT-29, breast adenocarcinoma MCF-7 cells as well as mouse fibroblast 3T3-L1 cells using XTT assay. Anticancer mechanistic studies were carried out with flow cytometry. XTT results revealed that all compounds exhibited dose-dependent anti-proliferative activity against the tested cancer cells, and especially compound 2 showed the strongest anti-proliferative activity with an IC50 value of 7.31 µM and the highest selectivity (15.74) on MCF-7 cells. Flow cytometry results confirmed that the cytotoxic power of the compound 2 on MCF-7 cells is closely related to the mitochondrial membrane damage, caspase activation, and apoptosis orientation. Finally, molecular docking studies were applied to determine the interactions between compound 2 and caspase-3 via in-silico approaches. By molecular docking studies, free binding energy (ΔGBind), docking score, Glide score values as well as amino acid residues in the active binding site were determined. Consequently, these results constitute a preliminary data for in vivo anticancer studies and have the potential as a chemotherapeutic agent.
Prediction of Molecular Conformation Using Deep Generative Neural Networks
Molecular conformations play a crucial role in fields such as materials science and drug design. Traditional methods like molecular dynamics and Monte Carlo simulations are limited in speed and accuracy. Deep learning models offer a promising solution by rapidly generating accurate molecular conformations. This Emerging Topic highlights recent progresses in using deep learning for predicting molecular conformations and explores the potential and challenges of this emerging field.
Abstract
The accurate prediction of molecular conformations with high efficiency is crucial in various fields such as materials science, computational chemistry and computer-aided drug design, as the three-dimensional structures accessible at a given condition usually determine the specific physical, chemical, and biological properties of a molecule. Traditional approaches for the conformational sampling of molecules such as molecular dynamics simulations and Markov chain Monte Carlo methods either require an exponentially increasing amount of time as the degree of freedom of the molecule increases or suffer from systematic errors that fail to obtain important conformations, thus presenting significant challenges for conformation sampling with both high efficiency and high accuracy. Recently, deep learning-based generative models have emerged as a promising solution to this problem. These models can generate a large number of molecular conformations in a short time, and their accuracy is comparable and, in some cases, even better than that of current popular open-source and commercial software. This Emerging Topic introduces the recent progresses of using deep learning for predicting molecular conformations and briefly discusses the potential and challenges of this emerging field.