Asia-Pacific Journal of Chemical Engineering最新文献

Conventionally, bulk liquid membranes (BLM) utilize hazardous chemicals as the carrier and diluent to prepare the membrane phase. This study presents a greener alternative by formulating BLM with a deep eutectic solvent (DES) as the membrane phase, thereby eliminating the need for separate chemicals as carrier and diluent. Cetyltrimethylammonium bromide (CTAB) and oleic acid (OA) were used as the hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD), respectively. This study identified the suitable HBA:HBD ratio for DES synthesis, characterized its physicochemical properties and extraction equilibria and evaluated the DES performance as the membrane phase in BLM. In this study, the HBA:HBD ratio was varied from 1:30 to 1:80. Fourier transform infrared spectroscopy and thermogravimetric analyses confirmed the formation of DES between CTAB and OA. Meanwhile, the extraction equilibria demonstrated the extraction capability of DES and confirmed the stoichiometry of Zn(II) ions extraction. The equilibrium constant was found to be 0.7167 and the stoichiometric ratio of 1:1 between DES and Zn ion. Finally, the maximum Zn(II) ions removal efficiency (89.30%) was achieved at an HBA:HBD ratio of 1:40 DES as the membrane phase. The results were obtained under conditions where the stirring speed, temperature and operation time were 300 rpm, 60°C and 240 min, respectively.

Increased worldwide demand for renewable energy has triggered considerable evolution of solar photovoltaic (PV) technology, which harnesses sunlight for electricity. Yet, one of the main problems in achieving PV maximum efficiency is heat, which the cells produce during operation, as high temperatures lower cell efficiency and overall performance. Therefore, heat generated is controlled using the proposed cooling method. The proposed research is based on edible oils such as olive oil, sunflower oil, and castor oil as coolant medium. These oils are used in first timeact as phase-changing material (PCM) in cooling the monocrystalline and polycrystalline panels through an incorporated oil box fixed on the rear side of the PV panel. Oil is allowed to flow from the storage tank to the rear side of the unit and into the collector tank. Thus, the heat of the front side PV panel is reduced. The research surface methodology (RSM) is used to optimize and enhance the efficiency. The significant inputs of the proposed analysis are Capacity (C), Time (T), Temperature (H), and the Output Power (P). The optimal condition for P is at [C] of 95%, [T] of 30 min, and [H] of 68.5°C. The critical results obtained for castor oil in monocrystalline and polycrystalline PV panels are reduction in temperature by 3.29°C and 4.35°C and increase in efficiency by 17.25% and 20.7%, respectively. Castor oil proved to be superior compared to other edible oil in terms of physical, chemical, and thermal properties and stability. Finally, the proposed methods are integrating the natural coolant selection, innovative backside cooling tank design, and easy oil management for enhanced thermal regulation and sustainable operation.

The pyrolysis kinetics of herb residues with potassium and iron addition were investigated using thermogravimetric analysis (TGA) and a four-logistic distribution activation energy model (4LG-DAEM). The model included hemicellulose, cellulose, lignin, and a pseudo-component for coke. Kinetic compensation effects and n-order mechanism functions were incorporated to better fit the catalytic pyrolysis process. Compared with conventional models, the 4LG-DAEM captured the TG curve characteristics more accurately, with R² values above 0.995. Fitted parameter analysis showed that potassium addition significantly reduced the activation energies of cellulose, hemicellulose, lignin, and biochar reactions, but narrowed the reaction temperature ranges. Iron addition had a smaller effect on activation energies but widened the reaction temperature range. The combination of potassium and iron had a synergistic effect, enhancing the secondary reactions of hemicellulose, cellulose, and coke more effectively than single catalysts. Potassium–iron composite catalysts notably increased the proportion of biochar secondary reactions in the total process. Additionally, the 4LG-DAEM model demonstrated strong predictive ability for the kinetics of potassium–iron catalyzed pyrolysis of various biomasses.

Pulse-jet cleaning technology is commonly used during the cleaning process of cartridge dust collectors. Understanding the changes in the airflow field and the transport laws of dust particles during the pulse-jet cleaning process is important to improve the cleaning efficiency. In the present study, a single cartridge dust collector was selected as the research object. The numerical simulations on the pulse-jet cleaning process of the cartridge were carried out by the CFD-DPM method. The distributions and variations of the airflow and the dust particles were investigated during the pulse-jet cleaning process for a filter cartridge. In order to optimize the flow field and improve the cleaning efficiency, new types of diffusers were designed, and simulations on the pulse-jet cleaning process were conducted. The distribution of the jet airflow, the pressure changes on the side walls of the filter cartridge, and the dust dispersion under the four conditions for the filter cartridge were obtained. It was found that the existence of the diffuser is helpful to broaden the coverage area of the pulse jet airflow, which is conducive to enhancing the effectiveness of the cleaning process. For the two-layer perforated diffuser, the dust concentration on the filter cartridge diminishes at the most rapid rate, from about 0.25 to 0.01 kg/m³, which is twice that of the condition without a diffuser. The present study provides a theoretical basis for the application of diffusers in the cleaning process and offers certain guidance for enhancing the cleaning efficiency of the filter cartridge dust collectors.

The escalating need for transient monitoring solutions within the body is driving significant advancements in bioresorbable electronics. Traditional diagnostic and monitoring methods are frequently costly, invasive, and time consuming. Conventional implantable sensors present further challenges due to their long-term presence. This can lead to complications and necessitate a second surgery for removal. Such removal ameliorates costs and patient distress and risks injury to the brain. These sensors can also act as a nidus for infection or immune-mediated inflammatory responses. The use of nonbiodegradable materials in these devices confers mainly to electronic waste and accelerates environmental pollution. Biodegradable sensors offer a compelling and key alternative. They are explicitly constructed to dissolve harmlessly in biofluids at well-defined programmable rates to biologically benign end products. This innovation ends the need for secondary surgical extraction, reduces the risk of infection, and alleviates environmental problems associated with electronic waste. This review gives a comprehensive overview of the current state of biodegradable sensors in biomedical applications. It emphasizes biomimetic composites, advanced materials such as polymers, metals, and novel substances, and distinct sensor designs including pressure, strain, electrochemical, optical, and temperature sensing. It also highlights key in vivo applications such as intracranial, cardiovascular, and general health monitoring. These devices fulfill the major clinical requirement for minimally invasive, temporary, and environmentally sustainable physiological monitoring.

Co-pyrolysis is potentially an effective treatment of oily sludge (OS) for oil recovery. The co-pyrolysis behavior and product distribution of OS and walnut shell (WS) were systematically investigated. Thermogravimetric analysis coupled with Fourier transform infrared spectroscopy (TG-FTIR) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) were employed to examine the effects of varying mixing ratios and temperatures on pyrolysis characteristics. The results revealed a clear synergistic interaction between OS and WS during co-pyrolysis. FTIR results showed that the addition of WS altered the reaction path, reduced CO₂ emissions, and changed the distribution of volatile products, resulting in improved pyrolysis efficiency and environmental performance. Kinetic analysis demonstrated that the co-pyrolysis was predominantly diffusion-controlled, with the strongest synergy and best model fit observed at a 1:1 OS-to-WS ratio at an OS-to-WS ratio of 1:1. In terms of product distribution, the incorporation of WS significantly reduced the yield of heavy components, thereby enhancing the quality and conversion efficiency of the pyrolysis oil. Pyrolysis temperature had a notable impact on the composition of volatile products, where acid compounds were markedly suppressed due to synergistic effects at temperatures of 600°C and below.

This study aims to investigate the research status and latest advancements in the application of magnetic resonance imaging (MRI) contrast agents for glioma diagnosis. A comprehensive search was conducted in the Web of Science Core Collection for literature published between 2015 and 2024 related to MRI, contrast agents, and glioma. A total of 454 relevant publications were included and analyzed using bibliometric tools, including the Online Analysis Platform and CiteSpace software. The number of publications peaked in 2017, followed by a gradual decline from 2021 to 2024. The United States, China, and Germany emerged as the leading countries in publication output. Prominent institutions included Johns Hopkins University, the University of California, Los Angeles (UCLA), and Oregon Health & Science University. The most frequently cited journals were Radiology, Neuro-oncology, and Journal of Magnetic Resonance Imaging. Among authors, Patrick Y. Wen was most cited, followed by Roger Stupp and Jerrold L. Boxerman. Influential references included “Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma” and “Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials.” Research hotspots focus on Nuclear Overhauser Enhancement (NOE), while emerging themes suggested increasing interest in radiotherapy, the blood–brain barrier (BBB), clinical recommendations, radiation necrosis, size, and deep learning applications. This bibliometric and visual analysis highlights the evolving research focus on MRI contrast agents in glioma diagnosis over the past decade. Future directions are likely to include advanced imaging techniques, nanotechnology-enhanced contrast agents, AI, and deep learning, clinical diagnostics, optimization, biomarker discovery, and personalized medicine approaches.

The resource utilization of food waste poses a pressing problem during the urbanization in China. In this paper, hydrothermal carbon (HC) was prepared from food waste by hydrothermal carbonization (HTC), and the combustion performance of HC was analyzed. The results indicated that the inorganic salts present in crab shells and bone meal within the food waste enhanced the ash content of the HC. The ash content of food waste HC with crab shells (FWH-CS) and food waste HC with bone meal (FWH-BM) was 18.43% and 30.26%, respectively. The removal of bones and crab shells from food waste increased the calorific value of the HC. The HC was subjected to K₂CO₃ activation to produce the activated HC (AHC) for Cu²⁺ adsorption. Static adsorption experiments revealed that the food waste AHC (FWA) exhibited the highest Cu²⁺ adsorption capacity (27.575 mg/g), whereas AHC from food waste with crab shells (FWA-CS) showed the lowest (23.9 mg/g). Combining the adsorption kinetics analysis, the adsorption mechanism for all three AHC samples predominantly involved chemisorption, supplemented by physisorption. Adsorption isotherm analysis indicated that both monolayer and multilayer adsorption processed in the three AHC samples.

The control challenges upturn if the non–self-regulating (integrating) processes include process time delay, double poles at the origin, and inverse behavior. None of the existing literature has considered all the above-mentioned types of processes in the frame of a unified control method. In this manuscript, a novel PD-PID–based dead time compensator is developed for a wide range of difficult integrating systems to achieve satisfactory performance and enhanced robustness against unwanted disturbance variables and process uncertainties. Analytical relations are derived for the inner and outer loop controllers using the direct synthesis approach. Further, suitable recommendations are made to choose the appropriate value of the tunable constants so that the robustness constraints are satisfied. The efficacy of the developed control laws is justified by implementing them on various case studies through numerical simulations and theoretical analysis. The suggested method is found capable of mitigating the effect of ramp-type disturbances also in addition to the step disturbance.

The traditional data-driven methods for predicting the component content of rare earth elements (REEs) suffer from several drawbacks, notably a considerable delay in data labeling, elevated costs, and a massive quantity of unused unlabeled data. To enhance the prediction of REE component content, this paper suggests an improved L2 regularization semi-supervised extreme learning machine based on DBSCAN and RBM, coined RBM-DL2SELM. First, to mitigate the potential issues of an unresolvable Moore–Penrose generalized inverse or model overfitting, which are common in traditional graph-based SELMs, the L2 regularization term is integrated into the SELM framework, forming the L2SELM model. This enhancement boosts the model's generalization ability. Second, the L2SELM model's structure is further refined by incorporating the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This step addresses concerns related to data imbalance in production processes, missing data entries, and potential data errors. Moreover, to overcome the instability issues stemming from the random initialization of weights and biases in the L2SELM, the restricted Boltzmann machine (RBM) is introduced for adaptive optimization to elevate both the prediction accuracy and reliability of the model. Finally, simulation verification using field data from Pr/Nd extraction demonstrates that the proposed method offers substantial advantages in terms of accuracy, stability, and data utilization. These attributes render it more suitable for practical applications in real-world scenarios, where vast amounts of unlabeled data are typically available at rare earth extraction sites.