ACS Omega最新文献

Curcumin from turmeric (Curcuma longa) has traditionally been used due to its pharmacological properties, such as anticancer, anti-inflammatory, cholesterol-lowering, and antioxidant activities, but has had limitations in use due to low bioavailability. Nanoparticles have protuberant efficacies to diagnose or cure a variety of diseases, including tumors, by fine-tuning their size, structure, and physicochemical characteristics. This study aims to develop a new dosage form of curcumin nanoparticles with zinc oxide to enhance its therapeutic efficacy against cancer and cause no damage to genetics. Curcumin zinc oxide nanoparticles were prepared and characterized by using a Zeta sizer, ultraviolet (UV)-spectrophotometer, scanning electron microscope (SEM), and Fourier transform infrared (FTIR) spectroscopy. Different concentrations range from 40 to 0.078 μg/mL, and these nanoparticles were evaluated for their anticancer activity by colorimetric analysis (MTT assay) on normal (Vero) and cancerous cell lines (MCF-7) and genotoxicity by the comet assay. The spherical-shaped curcumin zinc oxide nanoparticles of 189 nm size were prepared with characteristic functional groups. The selectivity index of curcumin zinc oxide nanoparticles, calculated from IC₅₀ values, is 4.60 > 2.0, showing anticancer potential comparable to tamoxifen. The genetic damage index of the highest concentration (40 μg/mL) of curcumin zinc oxide nanoparticles was 0.08, with a percent fragmentation of 8%. The results suggest that nanoparticles of curcumin zinc oxide produced better anticancer effects and did not cause any significant damage to the DNA. Consequently, further research is required to ensure the development of a safe and quality dosage form of nanoparticles for proper utilization.

Microalgal lipids contain a wide array of liposoluble bioactive compounds, but lipid extraction remains a critical limitation for their commercial use. An accelerated solvent extraction (ASE) was used to extract lipids from Chlamydomonas reinhardtii, Arthrospira platensis (Spirulina), and Chlorella vulgaris grown under either standard or nitrogen depletion conditions. Under standard growth conditions, ASE using methanol:chloroform (2:1), methyl tert-butyl ether (MTBE):methanol:water, and ethanol at 100 °C resulted in the highest recovery of total lipids (352 ± 30, 410 ± 32, and 127 ± 15 mg/g biomass from C. reinhardtii, C. vulgaris, and A. platensis, respectively). Similarly, the highest total lipid and triacylglycerols (TAGs) recovery from biomass cultivated under nitrogen depletion conditions was found at 100 °C using methanol:chloroform, for C. reinhardtii (total, 550 ± 21; TAG, 205 ± 2 mg/g biomass) and for C. vulgaris (total, 612 ± 29 mg/g; TAG, 253 ± 7 mg/g biomass). ASE with MTBE:methanol:water at 100 °C yielded similar TAG recovery for C. reinhardtii (159 ± 6 mg/g) and C. vulgaris (200 ± 4 mg/g). Thus, MTBE:methanol:water is suggested as an alternative substitute to replace hazardous solvent mixtures for TAGs extraction with a much lower environmental impact. The extracted microalgal TAGs were rich in palmitic (C16:0), stearic (C18:0), oleic (C18:1,9), linoleic (C18:2n6), and α-linolenic (C18:3n3) acids. Under nitrogen depletion conditions, increased palmitic acid (C16:0) recovery up to 2-fold was recorded from the biomasses of C. reinhardtii and C. vulgaris. This study demonstrates a clear linkage between the extraction conditions applied and total lipid and TAG recovery.

The scientific community needs to conduct research on novel electrodes for portable energy storage (PES) devices like supercapacitors (S–Cs) and lithium-ion batteries (Li-ion-Bs) to overcome energy crises, especially in rural areas where no electrical poles are available. Herein, the nanostructured MoS₂ and ZnS–MoS₂ E-Ms consisting of nanoparticles/rods/sheets (N-Ps-Rs-Ss) are deposited on hierarchical nickel foam by a homemade chemical vapor deposition (H-M CVD) route. The X-ray diffraction patterns confirm the formation of polycrystalline films growing along various orientations, whereas the field-emission scanning electron microscope analysis confirms the formation of N-Ps-Rs-Ss. The change in structural and microstructural parameters indicates the existence of defects improving the energy storage ability of the deposited ZnS–MoS₂@Ni–F electrodes. The specific capacitances of MoS₂@Ni–F and ZnS–MoS₂@Ni–F electrodes are found to be 1763 and 3565 F/g at 0.5 mV/s and 1451 and 3032 F/g at 1 A/g, respectively. The growing behavior of impedance graphs indicates their capacitive nature; however, the shifting of impedance curves toward y-axis indicates that the increasing diffusion rates due to the formation of nanostructures of ZnS–MoS₂ results in low impedance. An excellent energy storage performance, minimum capacity fading, and improved electrical conductivity of the deposited E-Ms are due to the combined contributions of the electrical double layer and pseudocapacitor nature, which is again confirmed by theoretical Dunn’s model. The absence of charge transfer resistance and good capacitance retention (95%) even after 10,000 cycles indicates that the deposited E-Ms are better for PES devices like S–Cs and Li-ion-Bs than MoS₂ E-Ms. The assembled asymmetric supercapacitor device exhibited the maximum specific capacitance = 996 F/g, energy density = 354–285 W h/kg, power density = 2400–24,000 W/kg, capacitance retention = 95% and Coulombic efficiency = 100% even after a long charging–discharging of 10,000 cycles.

Predicting the drug–target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an “image” and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network’s interpretability.

The two-dimensional (2D) materials class earned a boost in 2021 with biphenylene synthesis, which is structurally formed by the fusion of four-, six-, and eight-membered carbon rings, usually named 4-6-8-biphenylene network (BPN). This research proposes a detailed study of electronic, structural, dynamic, and mechanical properties to demonstrate the potential of the novel biphenylene-like indium nitride (BPN-InN) via density functional theory and molecular dynamics simulations. The BPN-InN has a direct band gap energy transition of 2.02 eV, making it promising for optoelectronic applications. This structure exhibits maximum and minimum Young modulus of 22.716 and 22.063 N/m, Poisson ratio of 0.018 and −0.008, and Shear modulus of 11.448 and 10.860 N/m, respectively. To understand the BPN-InN behavior when subjected to mechanical deformations, biaxial and uniaxial strains in armchair and zigzag directions from −8 to 8% were applied, achieving a band gap energy modulation of 1.36 eV over tensile deformations. Our findings are expected to motivate both theorists and experimentalists to study and obtain these new 2D inorganic materials that exhibit promising semiconductor properties.

The application of green chemistry is critical for cultivating environmental responsibility and sustainable practices in pharmaceutical manufacturing. Process mass intensity (PMI) is a key metric that quantifies the resource efficiency of a manufacturing process, but determining what constitutes a successful PMI of a specific molecule is challenging. A recent approach correlated molecular features to a crowdsourced definition of molecular complexity to determine PMI targets. While recent machine learning tools show promise in predicting molecular complexity, a more extensive application could significantly optimize manufacturing processes. To this end, we refine and expand upon the SMART-PMI tool by Sheridan et al. to create an open-source model and application. Our solution emphasizes explainability and parsimony to facilitate a nuanced understanding of prediction and ensure informed decision-making. The resulting model uses four descriptors─the heteroatom count, stereocenter count, unique topological torsion, and connectivity index chi4n─to compute molecular complexity with a comparable 82.6% predictive accuracy and 0.349 RMSE. We develop a corresponding app that takes in structured data files (SDF) to rapidly quantify molecular complexity and provide a PMI target that can be used to drive process development activities. By integrating machine learning explainability and open-source accessibility, we provide flexible tools to advance the field of green chemistry and sustainable pharmaceutical manufacturing.

The settling rate of the mineral fines in an aqueous solution changes depending on the charges they carry. Mineral fines with similar high-magnitude surface charges repel each other and prevent them from settling rapidly. In contrast, fines with no/low-magnitude surface charges can coalesce and agglomerate with the others and settle rapidly due to the increasing mass. This can lower the coagulant or flocculant use and speed up turbidity removal. Thus, considering this fact, the experimental tests in this study were performed below the neutral pH environment (pH 2–6) to determine the effectiveness of the coagulant and flocculant mixtures and compare the results with their single use. The turbidity removal tests were applied using different valence coagulants and flocculants with different charge mechanisms. According to the results with their single use, the best results were obtained using FeCl₃ (80 mg/L) at pH 4 with a turbidity removal efficiency of ≤98% and a nonionic flocculant at pH 2 with a turbidity removal efficiency ≥99% (0.50 mg/L). When they were used as binary mixtures, the lowest turbidity values were obtained with FeSO₄/nonionic flocculant mixtures at pH 4 (≤98%) and with FeCl₃/anionic flocculant mixtures at pH 2 (≥99%).

Nonribosomal peptide synthetases (NRPSs) are important enzymes that synthesize an array of nongenetically encoded peptides. The latter have diverse physicochemical properties and roles. NRPSs are modular enzymes in which, for example, the condensation (C-) domain catalyzes the formation of amide bonds. The NRPS tyrocidine synthetase from Brevibacillus brevis is responsible for synthesizing the cyclic-peptide antibiotic tyrocidine. The first step is formation of an amide bond between a proline and phenylalanine which is catalyzed by a C-domain. In this study, a multiscale computational approach (molecular dynamics and QM/MM) has been used to investigate substrate binding and catalytic mechanism of the C-domain of tyrocidine synthetase. Overall, the mechanism is found to proceed through three exergonic steps in which an active site Histidine, His222, acts as a base and acid. First, His222 acts as a base to facilitate nucleophilic attack of the prolyl nitrogen at the phenylalanyl’s carbonyl carbon. This is also the rate-limiting step with a free energy barrier of 38.8 kJ mol^–1. The second step is collapse of the resulting tetrahedral intermediate with cleavage of the S–C bond between the phenylalanyl and its Ppant arm, along with formation of the above amide bond. Meanwhile, the now protonated His222 imidazole has rotated toward the newly formed thiolate of the Ppant arm. In the final step, His222 acts as an acid, protonating the thiolate and regenerating a neutral His222. The overall mechanism is found to be exergonic with the final product complex being 46.3 kJ mol^–1 lower in energy than the initial reactant complex.

Nitrogen dioxide (NO₂) is a major pollutant, causing acid rain, photochemical smog, and respiratory damage. The annual safe limit is 50 parts per billion (ppb), while concentrations exceeding 1 part per million (ppm) can result in respiratory ailments. Conventionally, n-type metal oxide semiconductors operating at elevated temperatures have been utilized for NO₂ detection. Recently, p-type semiconductors with their hole accumulation layer, rapid recovery post-gas exposure, and good humidity tolerance are being investigated as potential NO₂ sensors, once again working at elevated temperatures. In this work, a room-temperature (27 ± 2 °C) NO₂ sensor is demonstrated by using a nanocomposite based on p-type bismuth ferrite (BFO) nanoparticles and silver nanowires (Ag NWs). This nanocomposite is capable of sensing a NO₂ gas concentration of up to 0.2 ppm. The BFO nanoparticles are synthesized via a sol–gel route followed by sintering at 500 °C to form the crystalline phase. Nanocomposites are obtained by formulating a dispersion of the BFO nanoparticles and Ag NWs, followed by direct writing on both flexible and rigid substrates. The Ag NWs act as the conducting pathway, reducing the overall electrical resistance and thus enabling room-temperature operation. X-ray diffraction, scanning electron microscopy, and surface area studies provide phase information and surface morphology, and the porous nature of the film helps in room-temperature gas adsorption. The current–voltage and gas-sensing behavior are studied to obtain the optimized molar ratio (4:1 BFO/Ag NWs) for the sensor. The sensor deposited on poly(ethylene terephthalate) (PET) also works under a bent condition, indicating good flexibility. Rapid NO₂ sensing was achieved in a BFO–Ag/PET device with response/recovery times of 7/8.5 s and 12/15 s in straight and bent geometries, respectively. Additionally, a good sensitivity of 30 to 60% was achieved for the BFO–Ag/PET device across 100 to 1000 ppb of NO₂. The development of a nanocomposite combining an active sensing element (BFO) and a charge-transport element (Ag NWs) opens up a multitude of other application areas.

Lithium–ion batteries with Li₃V₂(PO₄)₃/C as the cathode have been a popular research topic in recent years; however, studies of the effects of external magnetic fields on them are less common. This study investigates the effects of an external magnetic field applied parallel to the direction of the anode and cathode on the ion transport through iron-doped Li₃(V_1–xFe_x)₂(PO₄)₃, the outer carbon coating, the film/electrolyte/separator, and up to the lithium metal electrode on a microscopic level. The results reveal that for the x = 0.05 sample with lower doping, the magnetostriction expansion of Li₃(V_1–xFe_x)₂(PO₄)₃ and the magnetostrictive contraction effect of the outer ordered carbon layer cancel each other out, resulting in no significant enhancement of the battery’s energy and power density due to the external magnetic field. In contrast, the x = 0.1 sample, lacking magnetostrictive contraction in the outer ordered carbon layer, shows that its energy and power density can be influenced by the magnetic field. Under zero magnetic field, the cyclic performance exhibits superior average capacity performance in the x = 0.05 sample, while the x = 0.1 sample shows a lower decay rate. Both samples are affected by the magnetic field; however, the x = 0.1 sample performs better under magnetic conditions. In particular, in the C-rate tests under a magnetic field, the sample with x = 0.1 showed a significant relative reduction in capacity decay rate by 20.18% compared to the sample with x = 0.05.