ACS Omega最新文献

Leucoptera coffeella is a significant lepidopteran pest of coffee (Coffea spp.) crops, capable of causing yield losses of up to 80%. While chemical control with synthetic insecticides remains the predominant strategy, challenges such as pest resistance and environmental contamination highlight the urgent need for more sustainable alternatives. Hybrid polymeric membranes, formulated from Laponite RD clay, sodium alginate, and the insecticide cyantraniliprole, were developed in this study to serve as protective coatings for coffee leaves. Their successful synthesis and the effective integration and interaction of cyantraniliprole within the hybrid matrix were corroborated by proper characterization, including powder X-ray diffraction, Fourier-transform infrared spectroscopy with attenuated total reflectance, thermogravimetric analysis coupled with differential scanning calorimetry, and scanning electron microscopy with energy-dispersive spectroscopy. Bioassay experiments in a greenhouse using seedlings of Catuaí vermelho, a coffee variety of the Coffea arabica species, were conducted in a randomized block design with eight treatments and four replicates. Larval mortality and egg deposition were assessed and statistically analyzed using the Scott-Knott test (p < 0.05). The hybrid membranes significantly increased larval mortality and reduced oviposition compared to both control and commercial insecticide treatments. These findings underscore the potential of these membranes as an eco-friendly alternative for integrated pest management in coffee cultivation, offering advantages such as improved adhesion, sustained release, and reduced pesticide usage.

The rapid growth of the microbrewery sector, driven by the increasing demand for craft beers, has highlighted the importance of managing brewing residues to ensure environmental and economic sustainability. This review explores the primary byproducts of beer production─brewer’s spent grain (BSG), brewer’s spent yeast (BSY), and hot trub─analyzing their composition and potential for upcycling. By exploring the Brazilian microbrewery sector as a case study, this work highlights its growth and challenges, while drawing insights that resonate with the global interest in sustainable practices within the craft beer industry. Traditional applications, such as animal feed and fertilizer, are expanded with new approaches like biofuel production, biogas, biopolymer synthesis, prebiotics, and healthy food ingredient development. An emphasis on the applications of BSG in food formulations, such as bread, cookies, nutrition bars, beverages, meat analogs, yogurt, and others, highlights its nutritional value and functional properties for developing food products. By integrating sustainable practices and advanced processing technologies, brewing residues can be transformed into high-value products, reducing waste and fostering economic growth. This review concludes that adopting circular economy principles is essential for aligning microbrewery operations with global sustainability goals while unlocking the potential of brewing byproducts for diverse industrial applications.

This study explores the development of electrospun nanofibrous materials as delivery systems for the probiotic strain Lactobacillus paragasseri K7 (LK7) and the bioactive glycoprotein lactoferrin (LF) with applications targeting vaginal health. Electrospinning was used to encapsulate LK7 and LF into poly(ethylene oxide) (PEO)-based nanofibers supported on polypropylene fabric. Three formulations─PEO/LF, PEO/lactobacilli (LB) (with LK7), and PEO/LF/LB─were characterized for their physicochemical properties, fiber morphology (SEM), chemical composition (FTIR, XPS), and antioxidant activity (2,2′-azino-bis(3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS) assay). SEM analysis confirmed successful nanofiber formation, though LK7 remained on the fiber surface due to its size. FTIR and XPS analyses verified the incorporation of functional groups and elements associated with LF and LK7. The antioxidant assays showed that both LF and LK7 exhibited strong radical scavenging activity in formulations and it decreased slightly after electrospinning. Among the electrospun samples, the PEO/LF/LB formulation demonstrated the highest antioxidant potential. The viability and release studies revealed that 0.38–0.45% of LK7 survived during the electrospinning process and that the bacterial cells were released rapidly within 1 min of PBS exposure. Storage at 8 or 20 °C under 65% humidity reduced the viability (cfu) further, likely due to a transition to a viable but nonculturable (VBNC) state. Despite the low survival rates, the immediate release profile and antimicrobial potential of the materials support their suitability for short-term therapeutic applications such as vaginal tampons or wound dressings. This study highlights the potential of nozzle-free electrospinning for developing delivery systems for live biotherapeutics and postbiotics and suggests future work to optimize viability or expand into postbiotic applications.

Energy scarcity is now a global issue that motivates researchers to explore other energy sources. The purpose of this study was to investigate the possible uses of pig manure (PM) and khat waste (KW) substrate blends for biogas production. The two substrates, namely, PM and KW, initially were treated separately, followed by mixing for anaerobic digestion. In this process, the contribution of mixing ratio (75:25, 50:50, 25:75), temperature (25–45 °C), and hydraulic retention times (30–50 days) on the yield of biogas volume and methane percentage was studied through a central composite design (CCD) experimental design methodology. Consequently, the highest biogas volume of 1831.2 mL and methane percentage of 61.4 wt % were observed at a mixing ratio of 50:50 (PM/KW, %), 45 °C temperature, and hydraulic retention time of 40 days. In summary, the research indicates that utilizing pig manure in combination with khat waste for the production of biogas may be a possible alternative energy source that can address existing global energy challenges.

Organic photovoltaic devices hold significant promise for sustainable energy generation, due to their low manufacturing cost and benefits such as lightness, semitransparency, and flexibility. This study explores the experimental and theoretical study of the effects of sodium chloride (NaCl) doping on PEDOT:PSS, with a focus on its photovoltaic properties in inverted all air blade-coated devices. By varying NaCl concentrations, we analyze key performance metrics and annealing treatment, open-circuit voltage, short-circuit current density, fill factor, and power conversion efficiency. The results indicate that lower concentrations of NaCl substantially improve these parameters, while higher concentrations can impair the efficiency of the device. Post annealing was found to improve photovoltaic performance, especially in samples with low NaCl concentrations. Advanced characterization techniques, including atomic force microscopy, Raman spectroscopy, and scanning electron microscopy, revealed that doping with NaCl improves the molecular organization of PEDOT:PSS, leading to better light transmission and energy efficiency. Theoretical results indicate that NaCl interacts with the thiophene rings of PEDOT:PSS, modifying its electronic and structural properties. These results highlight the fundamental role of doping and processing conditions in optimizing the performance of organic solar cells, providing valuable information for the development of efficient, economical, and sustainable photovoltaic technologies.

New methodologies for the treatment of cancer continue to be developed, with an emphasis on improved specificity and targeted delivery to tumor cells. An innovative crumpled graphene oxide (CGO)-based drug delivery system (DDS) was fabricated via a single-step aerosol methodology to achieve reduced material toxicity and to enhance the drug-loading capacity for an anticancer drug doxorubicin (DOX). Specifically, the CGO-based systems feature reduced particle sizes and preserved surface modifiability compared with the current graphene oxide (GO)-based systems. In addition, the CGO-based drug delivery system can be loaded with the drug DOX by either surface attachment or one-step aerosol-based encapsulation. The particle size, drug-loading capacity, drug release profile, and in vivo toxicity of the synthesized DDS nanoparticles were evaluated in this study. The synthesis for the CGO DDS was a one-step aerosolized approach in a furnace aerosol reactor (FuAR). The synthesized structures were loaded with the drug to form composites, with DOX either loaded on the surface or encapsulated inside the structure. The one-step aerosol synthesis process produced CGO particles with a significantly smaller particle size (350.55 ± 91.43 nm) than GO (635.32 ± 80.41 nm), with a similar loading capacity for DOX (0.52 ± 0.01 mg/mg) compared to sheet GO (0.57 ± 0.07 mg/mg). At a pH of 7.4, the CGO loaded with DOX composite exhibited a similar asymptotic percentage of release (11.23%) over a 24 h period compared to the GO composite (13.12%). In vivo toxicity studies on mice indicated that CGO demonstrated less toxicity (60 mg/kg) in mice than sheet GO (7.5 mg/kg) in the maximum tolerated dose (MTD). Thus, the crumpled graphene oxide particles, along with those pretreated polyethylene glycol (PEG) surface modifications, can offer solutions for developing a suitable drug delivery system for cancer treatment.

Cobalt and its alloys are essential in many advanced technologies and understanding their mechanical properties at the nanoscale is crucial for designing next-generation materials. In this work, an angular-dependent potential for cobalt was developed by fitting to a reference data set of atomic forces, energies, and stress tensors derived from first-principles density functional theory calculations. The potential’s performance was systematically evaluated against experimental data and two established classical potentials─an embedded-atom method potential and a modified embedded-atom method potential─across a range of structural, mechanical, thermal, and defect properties for both HCP and FCC phases, as well as the liquid state. The ADP model demonstrates a favorable balance between accuracy and computational cost, exhibiting a mean absolute percentage error of 6.3% for mechanical and elastic properties. Large-scale molecular dynamics simulations of nanoindentation on the (0001) basal plane of HCP cobalt were performed to investigate the atomistic mechanisms of plastic deformation. The simulations reveal that plasticity initiates with the nucleation of <a>-type dislocations on basal planes, followed by the activation of pyramidal <c+a> slip and a localized, reversible HCP-to-FCC phase transformation under high pressure. The critical shear stress for dislocation nucleation was found to decrease with increasing indenter radius, converging to a value of (13.7 ± 0.6) GPa.

Hydrogen (H₂) is gaining momentum as a clean energy carrier, yet large-scale storage remains a major challenge. Storing hydrogen in depleted gas reservoirs is a promising option, leveraging existing infrastructure and proven containment reliability. A key factor influencing storage efficiency is the surface tension (ST) at the gas–brine interface, which controls capillary trapping and fluid flow. However, direct ST measurements are costly and time-consuming, motivating the use of predictive tools. This study applies machine learning (ML) algorithms to model ST between hydrogen–methane mixtures and brine under reservoir conditions. A data set of 1050 experimental measurements was used to train and test several ML algorithms. The models include an Adaptive Neuro-Fuzzy Inference System (ANFIS) and artificial neural networks (ANNs) optimized via Bayesian regularization. Model performance was evaluated using the coefficient of determination (R²), mean squared error (MSE), and mean absolute percentage error (MAPE). Model-agnostic methods such as partial-dependence plots and permutation feature importance were used for interpretation. All models achieved strong predictive performance (R² > 0.96), significantly better than the ST estimated by empirical correlations. The cascade-forward backpropagation neural network (CFBN) showed the highest training accuracy (R² = 0.9919, MSE = 0.001, MAPE = 0.334), while the feedforward neural network (FNN) performed best in testing (R² = 0.9849, MSE = 0.0019, MAPE = 0.286). ANFIS yielded the lowest MAPE in both training (0.0186) and testing (0.0200). Density difference was identified as the most influential feature. These results confirm that ML models can efficiently and accurately predict ST in underground hydrogen storage systems, reducing experimental demands and accelerating site evaluation.

In response to the issues of missed detection, false detection, and low accuracy often encountered in single-modal ore sorting under complex environments, this paper proposes an intelligent coal–gangue sorting method based on the fusion of industrial CCD (Charge Coupled Device) images and DE-XRT (Dual Energy X-ray Transmission) images. First, a data acquisition device was set up in the laboratory to collect CCD and DE-XRT images of coal and gangue. Adaptive weighted fusion was applied to preprocess the high- and low-energy X-ray images. Subsequently, we constructed a dual-branch backbone network capable of simultaneously processing CCD and XRT images. To achieve a balance between efficiency and performance, this network is built upon a lightweight MobileNetV4 architecture, where the width multiplier is adjusted to significantly reduce the parameters and computational complexity. Furthermore, the SimAM attention mechanism is incorporated to enhance feature representation and improve recognition accuracy. The proposed method effectively combines the advantages of CCD images (providing clear surface features) and XRT images (enabling internal component identification) and achieves real-time accurate ore sorting. Experimental results demonstrate that the image fusion approach maintains excellent performance while significantly reducing model complexity: compared to the single-modal CCD and XRT models, our fusion model reduces FLOPs by 64.9%, decreases parameters by 61.7%, and improves recognition accuracy by 7.6% and 28.3%, respectively. Finally, the model achieved 100% recognition accuracy, with no missed or false detections.