Effectiveness of Person-Centered Health Education in the General Practice of Geriatric Chronic Disease Care.

Objective: This study assesses the impact of personalized health education on elderly patients with chronic diseases in a general practice setting. The rationale behind the incorporation of personalized health education stems from the growing recognition of the need for patient-centered care approaches, particularly in geriatric populations, where such interventions could lead to improved health outcomes. Our study aims to evaluate the effects of personalized health education on elderly patients with chronic diseases in a general practice context. The initiation of this study is grounded in the increasing acknowledgment of patient-centered care's significance, especially in geriatric demographics. We hypothesize that personalized health education interventions could significantly enhance health outcomes in this patient group. Methods: We conducted a randomized controlled trial involving 126 elderly patients with chronic diseases, assigning them equally to receive either standard care or standard care supplemented with personalized health education. The effectiveness of this education was measured through outcomes related to cognition, self-care, health literacy, psychological and physical health, quality of life, and prognosis. In our study, we executed a randomized controlled trial encompassing 126 elderly patients diagnosed with a range of chronic diseases. These participants were evenly divided into two groups: one receiving standard care and the other receiving standard care enhanced with personalized health education. The study spanned over a specified period, during which the impact of the personalized health education was meticulously evaluated. To comprehensively measure the effectiveness of the personalized health education, we employed a variety of tools and scales. These instruments were specifically chosen to assess changes in cognition, self-care abilities, health literacy, and psychological and physical health. Additionally, we evaluated the quality of life and prognosis of these patients, aiming to capture the holistic impact of the intervention. This approach ensured a thorough and nuanced understanding of how personalized health education influences the health outcomes of elderly patients with chronic diseases. Results: The intervention group demonstrated significant improvements across all measured outcomes compared to the control group, highlighting the efficacy of personalized health education in enhancing comprehensive health parameters in geriatric patients with chronic conditions. In our study, the intervention group, which received personalized health education, exhibited notable improvements in several key areas compared to the control group. Specifically, there was a marked enhancement in cognition and health literacy, with patients showing improved understanding and management of their conditions. Additionally, significant gains were observed in the quality of life, indicating that the tailored health education effectively addressed the holistic needs of geriatric patients with chronic diseases. These specific findings underscore the substantial impact of personalized health education in improving critical health outcomes in this patient population. Conclusion: Personalized health education in geriatric chronic disease management significantly betters disease comprehension, health literacy, self-care, psychological well-being, and physical health while also lowering the risk of adverse events. This study underscores the value of patient-centered educational strategies in chronic disease care for the elderly.Our study conclusively demonstrates that personalized health education plays a pivotal role in managing chronic diseases among the elderly. It significantly improves disease comprehension, health literacy, self-care capabilities, psychological well-being, and physical health. Furthermore, it contributes to a reduced risk of adverse health events. These findings emphasize the critical importance of integrating patient-centered educational strategies into general practice care for the elderly. By doing so, we can enhance their overall well-being and quality of life, making personalized health education an essential component in the holistic care of elderly patients with chronic conditions. This approach not only aligns with the principles of modern geriatric care but also sets a benchmark for the future of chronic disease management in older populations.

Life cycle assessment perspective on waste resource utilization and sustainable development: A case of glyphosate production.

The growing demand for pesticide manufacturing and increasing public awareness of sustainable development, have let to urgent requirements for a refined environmental management framework. It is imperative to conduct process-based life cycle assessments (LCAs) to promote clean and environment-friendly technologies. Herein, the cradle-to-gate LCA of glyphosate production was executed as an example to investigate crucial production factors (materials or energy) and multiple environmental impacts during the production processes. Results showed that methanol caused the highest environmental damage in terms of toxicity, with a normalized value of 85.7 × 10-8, followed by coal-fired electricity in 6.00 × 10-8. Furthermore, optimized schemes were proposed, including energy improvement (electricity generated by switching from coal-fired power to solar power) and wastewater targeted conversion. Regarding the normalization results before and after optimization, the latter showed more significant results with the normalized value decreasing by 21.10 × 10-8, while that of the former only decreased by 6.50 × 10-8. This study provides an integrated LCA framework for organophosphorus pesticides (OPs) from upstream control and offers an important supplement to managing the key pollution factors and control links of the OP industry. Moreover, it reveals the positive influence of optimized schemes in facilitating cleaner production technologies, thus ultimately promoting new methodologies for resource recycling.

A supplementary assessment system of AQI-V for comprehensive management and control of air quality in chemical industrial parks.

Volatile organic compounds (VOCs) are the dominant pollutants in industrial parks. However, they are not generally considered as part of the air quality index (AQI) system, which leads to a biased assessment of pollution in industrial parks. In this study, a supplementary assessment system of AQI-V was established by analyzing VOCs characteristics with vehicle-mounted PTR-TOFMS instrument, correlation analysis and the standards analysis. Three hourly and daily scenarios were considered, and the hierarchical parameter setting was further optimized by field application. The hourly and daily assessments revealed the evaluation factors for the discriminability of different air quality levels, practiced value for regional air quality improvement, and the reservation of general dominant pollutants. Finally, the universality testing in ZPIP successfully recognized most of the peaks, with 54.76%, 38.39% and 6.85% for O3, VOCs and NO2 as the dominant pollutant, and reflected the daily ambient air quality condition, together with the dominant pollutant. The AQI-V system with VOCs sub-index is essential for air quality evaluation in industrial parks, which can further provide scientific support to control the pollution of VOCs and the secondary pollutant, therefore significantly improve the air quality in local industrial parks.

A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction.

BACKGROUND: Early risk stratification is important for patients with acute myocardial infarction (AMI). We aimed to develop a simple APACHE IV dynamic nomogram, combined with easily available clinical parameters within 24 h of admission, thus improving its predictive power to assess the risk of mortality at 28 days. METHODS: Clinical information on AMI patients was extracted from the eICU database v2.0. A preliminary XGBoost examination of the degree of association between all variables in the database and 28-day mortality was conducted. Univariate and multivariate logistic regression analysis were used to perform screening of variables. Based on the multifactorial analysis, a dynamic nomogram predicting 28-day mortality in these patients was developed. To cope with missing data in records with missing variables, we applied the multiple imputation method. Predictive models are evaluated in three main areas, namely discrimination, calibration, and clinical validity. The discrimination is mainly represented by the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Calibration is represented by the calibration plot. Clinical validity is represented by the decision curve analysis (DCA) curve. RESULTS: A total of 504 people were included in the study. All 504 people were used to build the predictive model, and the internal validation model used a 500-bootstrap method. Multivariate analysis showed that four variables, APACHE IV, the first sample of admission lactate, prior atrial fibrillation (AF), and gender, were included in the nomogram as independent predictors of 28-day mortality in AMI. The prediction model had an AUC of 0.819 (95%CI 0.770-0.868) whereas the internal validation model had an AUC of 0.814 (95%CI 0.765-0.860). Calibration and DCA curves indicated that the dynamic nomogram in this study were reflective of real-world conditions and could be applied clinically. The predictive model composed of these four variables outperformed a single APACHE IV in terms of NRI and IDI. The NRI was 16.4% (95% CI: 6.1-26.8%; p = 0.0019) and the IDI was 16.4% (95% CI: 6.0-26.8%; p = 0.0020). Lactate accounted for nearly half of the total NRI, which showed that lactate was the most important of the other three variables. CONCLUSION: The prediction model constructed by APACHE IV in combination with the first sample of admission lactate, prior AF, and gender outperformed the APACHE IV scoring system alone in predicting 28-day mortality in AMI. The prediction dynamic nomogram model was published via a website app, allowing clinicians to improve the predictive efficacy of the APACHE IV score by 16.4% in less than 1 min.

Erythema Nodosum following Nocardia Infection: A Case Report.

Cutaneous nocardiosis is a rare bacterial infection that can result in various dermatologic manifestations such as actinomycetoma, lymphocutaneous infection, superficial skin infection, and secondary infection due to hematogenous dissemination. We report on a Chinese patient with erythema nodosum-like exanthema, possibly secondary to nocardiosis. Our diagnosis for this patient was based on the clinical presentation, histopathological evidence, and microbiological findings. Given the protean manifestation of Nocardia, persistent reports on new presentations of the disease are important for early identification and treatment.

A novel approach for VOC source apportionment combining characteristic factor and pattern recognition technology in a Chinese industrial area.

Volatile organic compound (VOC) emission control and source apportionment in small-scale industrial areas have become key topics of air pollution control in China. This study proposed a novel characteristic factor and pattern recognition (CF-PR) model for VOC source apportionment based on the similarity of characteristic factors between sources and receptors. A simulation was carried out in a typical industrial area with the CF-PR model involving simulated receptor samples. Refined and accurate source profiles were constructed through in situ sampling and analysis, covering rubber, chemicals, coating, electronics, plastics, printing, incubation and medical treatment industries. Characteristic factors of n-undecane, styrene, o-xylene and propane were identified. The source apportionment simulation results indicated that the predicted contribution rate was basically consistent with the real contribution rate. Compared to traditional receptor models, this method achieves notable advantages in terms of refinement and timeliness at similar accuracy, which is more suitable for VOC source identification and apportionment in small-scale industrial areas.

A Novel Dual-Stage Phase Separation Process for CO2 Absorption into a Biphasic Solvent with Low Energy Penalty.

An amine-based biphasic solvent is promising to cut down the energy penalty of CO2 capture. However, the high viscosity of the CO2-enriched solvent retards its industrial application. This work proposed a novel dual-stage phase separation process using a triethylenetetramine and 2-(diethylamino)ethanol blend as a biphasic solvent, which separates a certain proportion of CO2-enriched phase during CO2 absorption to reduce its viscosity. Experimental results showed that the proposed dual-stage phase separation process improved the phase separation behavior and effectively enhanced the absorption rate by 49% at 50 °C, when 50 vol % CO2-enriched phase was separated at 0.3 mol mol-1. Kinetic analysis showed that the absorption rate was mainly controlled by liquid-side mass transfer. The regeneration heat of the dual-stage phase separation process cut down the energy penalty by 33% compared with the monoethanolamine-based process. Compared with the conventional biphasic solvent-based process, the heat duty was further declined by 8%. The 1H nuclear magnetic resonance analysis showed that the dual-stage phase separation process could effectively control the generation of absorption products and intensify the interphase migration of tertiary amines.

Novel Amino-Functionalized Ionic Liquid/Organic Solvent with Low Viscosity for CO2 Capture.

To achieve low regeneration energy consumption and viscosity, a novel amino-functionalized ionic liquid [TEPAH][2-MI] combined with organic solvents has been proposed for CO2 capture in this work. The results demonstrated that the absorption loading of [TEPAH][2-MI]/N-propanol (NPA)/ethylene glycol (EG) was 1.72 mol·mol-1 (28 wt %, 257 g·L-1), which was much higher than that of monoethanolamine/water, and the regeneration efficiency was maintained at 90.7% after the fifth regeneration cycle. The viscosities of the solution were only 3.66 and 7.65 mPa·s before and after absorption, respectively, which were significantly lower than those of traditional nonaqueous absorbents. The reaction mechanism investigated via 13C NMR and quantum calculations summarized that CO2 first reacted with the amino group of [TEPAH]+ to form the carbamates through the zwitterion formation and protonation process, while CO2 reacted with the N atom of [2-MI]- to directly form the carbamate. Then, some of them further reacted with NPA and EG to form the carbonates. Moreover, Nπ and Nτ tautomers of [TEPAH][2-MI] could convert into each other continuously when CO2 was absorbed. During CO2 desorption, the carbamates and carbonates reacted with AFILH+ to decompose and released CO2 directly.

Multi-factorial analysis of the removal of dichloromethane and toluene in an airlift packing bioreactor.

Biotechnology has proven effective in removing a wide variety of VOCs. In this study, the effects of pH (from 3 to 7), operating temperature (20-30 °C), empty bed residence time (EBRT, 10-40 s) and transient inlet concentration (400-4000 mg m-3) on the removal performance of an airlift packing bioreactor (ALPR) was investigated. The removal efficiency (RE) and stability of the ALPR was evaluated and compared with the conventional airlift bioreactor (ALR). The results showed that under the influence of single factor variation, the ALPR showed significant higher RE and better stability than the ALR in removing dichloromethane (DCM) and toluene. Besides, a factorial design was used to analyses the interaction of multiple factors and their influence on the removal of DCM and toluene in the ALPR and ALR. It shows that pH value has the most significant influence, and plays a crucial role in maintaining high RE of DCM and toluene in both of the ALPR and ALR. Temperature has a great effect on the removal of toluene. EBRT has certain effect on the removal of DCM in the ALPR. The transient concentration of a single substrate has a significant negative effect on the RE of this substrate, while it does not significantly affect the removal of another substrate in the ALPR. However, the steep increase of DCM concentration has an adverse effect on the RE of high concentration toluene in the ALR. The overall RE and degradation capacity of both toluene and DCM by the ALPR are much higher than that of the conventional ALR.

An integrated chemical mass balance and source emission inventory model for the source apportionment of PM2.5 in typical coastal areas.

The source apportionment of PM2.5 is essential for pollution prevention. In view of the weaknesses of individual models, we proposed an integrated chemical mass balance-source emission inventory (CMB-SEI) model to acquire more accurate results. First, the SEI of secondary component precursors (SO2, NOx, NH3, and VOCs) was compiled to acquire the emission ratios of these sources for the precursors. Then, a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components (SO42-, NO3-, NH4+, and SOC). Afterwards, the contributions of secondary components were apportioned into primary sources according to the source emission ratios. The final source apportionment results combined the contributions of primary sources by CMB and SEI. This integrated approach was carried out via a case study of three coastal cities (Zhoushan, Taizhou, and Wenzhou; abbreviated WZ, TZ, and ZS) in Zhejiang Province, China. The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources. The SEI results indicated that electricity, industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors. The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources, electricity production sources and industrial production sources. Compared to the results of the CMB and SEI models alone, the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.

Microbial compositions and metabolic interactions in one- and two-phase partitioning airlift bioreactors treating a complex VOC mixture.

Engineered microbial ecosystems in bioscrubbers for the treatment of volatile organic compounds (VOCs) have been complicated by complex VOC mixtures from various industrial emissions. Microbial associations with VOC removal performance of the bioscrubbers are still not definitive. Here, one- and two-phase partitioning airlift bioreactors were used for the treatment of a complex VOC mixture. Microbial characteristics in both bioreactors were uncovered by high-throughput metagenomics sequencing. Results showed that dominant species with specialized VOC biodegradability were mainly responsible for high removal efficiency of relative individual VOC. Competitive enzyme inhibitions among the VOC mixture were closely related to the deterioration of removal performance for individual VOC. Relative to the mass transfer resistance, the specialized biodegrading functions of microbial inoculations and enzymatic interactions among individual VOC biodegradation also must be carefully evaluated to optimize the treatment of complex VOC mixtures in bioreactors.

A Biophysicochemical Model for NO Removal by the Chemical Absorption-Biological Reduction Integrated Process.

The chemical absorption-biological reduction (CABR) integrated process is regarded as a promising technology for NOx removal from flue gas. To advance the scale-up of the CABR process, a mathematic model based on mass transfer with reaction in the gas, liquid, and biofilm was developed to simulate and predict the NOx removal by the CABR system in a biotrickling filter. The developed model was validated by the experimental results and subsequently was used to predict the system performance under different operating conditions, such as NO and O2 concentration and gas and liquid flow rate. NO distribution in the gas phase along the biotrickling filter was also modeled and predicted. On the basis of the modeling results, the liquid flow rate and total iron concentration were optimized to achieve >90% NO removal efficiency. Furthermore, sensitivity analysis of the model revealed that the performance of the CABR process was controlled by the bioreduction activity of Fe(III)EDTA. This work will provide the guideline for the design and operation of the CABR process in the industrial application.

Bioelectrochemical Reduction of Fe(II)EDTA-NO in a Biofilm Electrode Reactor: Performance, Mechanism, and Kinetics.

A biofilm electrode reactor (BER) is proposed to effectively regenerate Fe(II)EDTA, a solvent for NOx removal from flue gas, from Fe(II)EDTA-NO, a spent solution. In this study, the performance, mechanism, and kinetics of the bioelectrochemical reduction of Fe(II)EDTA-NO were investigated. The pathways of Fe(II)EDTA-NO reduction were investigated via determination of nitrogen element balance in the BER and an abiotic electrode reactor. The experimental results indicate that the chelated NO (Fe(II)EDTA-NO) is reduced to N2 with N2O as an intermediate. However, the oxidation of NO occurred in the absence of Fe(II)EDTA in abiotic reactors. Furthermore, the accumulation of N2O was suppressed with the help of electricity. The preponderant electron donor for reduction of Fe(II)EDTA-NO was also confirmed via analysis of the electron conservation. About 87% of Fe(II)EDTA-NO was reduced using Fe(II)EDTA as the electron donor in the presence of both glucose and cathode electrons while the cathode electrons were utilized for the reduction of Fe(III)EDTA to Fe(II)EDTA. Michaelis-Menten kinetic constants of bioelectrochemical reduction of Fe(II)EDTA-NO were also calculated. The maximum reduction rate of Fe(II)EDTA-NO was 13.04 mol m(-3) h(-1), which is 50% higher than that in a conventional biofilter.

Generation, utilization, and transformation of cathode electrons for bioreduction of Fe(III)EDTA in a biofilm electrode reactor related to NOx removal from flue gas.

A chemical absorption-biological reduction (CABR) integrated system, which employs iron chelate as a solvent, is under development for NOx removal from flue gas. Biofilm electrode reactor (BER) is deemed as a promising bioreactor to regenerate the iron chelate. Although it has been proved that BER can significantly enhance the bioreduction of Fe(III)EDTA, the bioelectrochemistry mechanism involved in the bioreduction of Fe(III)EDTA remains unknown. This work aims to explore this mechanism via the analysis of the generation, utilization, and transformation of cathode electrons in the BER. The results indicate that the generation of cathode electrons follows Faraday's law. The generated cathode electrons were used to produce H2 and directly reduce Fe(III)EDTA in the BER. Meanwhile, the produced H2 served as an electron donor for bioreduction of Fe(III)EDTA. The excess H2 product was transformed to simple organics, e.g., methanol by the hydrogen autotrophy of Pseudomonas under the inorganic and anaerobic conditions. Overall, this work revealed that the reduction of Fe(III)EDTA in the BER was enhanced by both direct electrochemical reduction and indirect bioreduction using H2 as an intermediate. It is also interesting that the excess H2 product was transformed to methanol for microbial metabolism and energy storage in the BER.

Degradation of ethyl mercaptan and its major intermediate diethyl disulfide by Pseudomonas sp. strain WL2.

A Pseudomonas sp. strain WL2 that is able to efficiently metabolize ethyl mercaptan (EM) into diethyl disulfide (DEDS) through enzymatic oxidation was isolated from the activated sludge of a pharmaceutical wastewater plant. One hundred percent removal of 113.5 mg L(-1) EM and 110.3 mg L(-1) DEDS were obtained within 14 and 32 h, respectively. A putative EM degradation pathway that involved the catabolism via DEDS was proposed, which indicated DEDS were further mineralized into carbon dioxide (CO2), bacterial cells, and sulfate (SO4 (2-)) through the transformation of element sulfur and ethyl aldehyde. Degradation kinetics for EM and DEDS with different initial concentrations by strain WL2 were evaluated using Haldane-Andrews model with maximum specific degradation rates of 3.13 and 1.33 g g(-1) h(-1), respectively, and maximum degradation rate constants of 0.522 and 0.175 h(-1) using pseudo-first-order kinetic model were obtained. Results obtained that aerobic degradation of EM by strain WL2 was more efficient than those from previous studies. Substrate range studies of strain WL2 demonstrated its ability to degrade several mercaptans, disulfides, aldehydes, and methanol. All the results obtained highlight the potential of strain WL2 for the use in the biodegradation of volatile organic sulfur compounds (VOSCs).

Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park.

Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park. It converts a large amount of monitoring data with high spatiotemporal complexity into simple and interpretable characteristic maps, effectively improving the identification effect of VOC emission characteristics, supporting more accurate source apportionment of VOC pollution around the park. Using this method, the VOC emission characteristics of eight key factories were identified. VOC source apportionment in the park was conducted for one day using a positive matrix factorization (PMF) model and seven combined factor profiles (CFPs) were calculated. Based on the identified VOC emission characteristics, the main pollution sources and their contributions to surrounding schools and residential areas were determined, revealing that one pesticide factory (named LKA) had the highest contribution ratio. The source apportionment results indicated that the impact of the chemical industrial park on the surrounding areas varied from morning to afternoon, which to some extent reflected the intermittent production methods employed for fine chemicals.

Analysis of Risk Factors for Urinary Tract Infection in Children and Construction and Validation of a Prediction Model.

OBJECTIVE: To analyse the risk factors for urinary tract infection (UTI) in children and construct and validate a risk prediction model. METHODS: The study selected 258 children with suspected UTI in the paediatric department of our hospital from August 2019 to August 2021. Identified as the subjects in this research, paediatric patients' clinical data were used for retrospective analysis. Based on the counting results of urinary leucocytes and bacteria, children were divided into the UTI group (n = 67) and non-UTI group (n = 191). Univariate analysis and multivariate logistic regression analysis were used to screen the independent risk factors for UTI in children, and a prediction model was constructed according to the results. The Hosmer-Lemeshow goodness-of-fit (GOF) test and receiver operator characteristic (ROC) curve analysis were used to validate the calibration and application value of prediction model. RESULTS: Logistic regression analysis identified length of hospitalisation ≥10 days (OR = 3.611, 95% CI: 1.781-7.325), indwelling ureter (odds ratio (OR) = 3.203, 95% CI: 1.615-6.349), history of infection (OR = 4.827, 95% CI: 2.424-9.612), congenital malformation/dysplasia (OR = 4.212, 95% CI: 2.079-8.531), constipation (OR = 4.021, 95% CI: 1.315-12.299) and anaemia (OR = 2.275, 95% CI: 1.236-4.186) as risk factors for UTI in children (p < 0.05). The formulation method was adopted to construct the following prediction model of UTI in children: Z = 2.066 × (length of hospitalisation ≥10 days) + 1.164 × (indwelling ureter) + 1.574 × (history of infection) + 1.438 × (congenital malformation/dysplasia) + 1.392 × (constipation) + 0.882 × (anaemia). The test results revealed the good GOF and high calibration (χ2 = 9.077, p = 0.336) of prediction model. Furthermore, the area under the ROC curve was 0.825 (95% CI: 0.766-0.884, p < 0.001), indicating the good discrimination and prediction efficiency of model. CONCLUSIONS: Based on clinical results, further attention should be given to high-risk children with UTI, and intervention measures should be taken immediately. The application and popularisation of prediction model will allow us to provide strategic guidance for preventing and treating UTIs in clinics.

Medical Applications of Hydrogels in Skin Infections: A Review.

Skin infections are common diseases for which patients seek inpatient and outpatient medical care. Globally, an increasing number of people are affected by skin infections that could lead to physical and psychological damage. Skin infections always have a broad spectrum of clinical presentations that require physicians to make an aggressive and accurate diagnosis for prescribing the proper symptomatic antimicrobials. In most instances, the treatment for skin infections mainly includes oral or topical anti-infective drugs. However, some of the classical anti-infective drugs have limitations, such as poor water solubility, low bioavailability, and poor targeting efficiency, which can lead to poor efficacy, adverse effects, and drug resistance. Therefore, research priorities should focus on the development of more effective drug delivery systems with new materials. Hydrogels are a highly multifunctional class of medical materials with potential applications in dermatology. Several hydrogel dressings with anti-infective functions have been formulated and demonstrated to improve the efficacy and tolerance of oral or topical classical anti-infective drugs to a certain degree. In this study, the medical applications of hydrogels for the treatment of various skin infections are systematically reviewed to provide an important theoretical reference for future research studies on the treatment options for skin infections.

Quantifying the energy-material-pollution nexus in a typical fine chemical industry: A sustainable development-oriented support for collaborative emission reduction.

The fine chemical industry is currently facing challenges in energy saving, material conservation, and pollution reduction due to the dual policy pressure of precise system management and collaborative pollution and carbon reduction. However, the interweaving of materials and energy input-output was not well understood due to the incomplete coverage and the lack of a generic framework. Therefore, a methodology based on the energy-material-pollution (E-M-P) coupling nexus was proposed to quantitatively assess multi-level coupling. According to the selected generic 32 coupling units, two representative glyphosate (PMG) production processes were taken as case studies. Quantification results showed that the solvent element and the material system had a higher priority. Moreover, Process 2 owned a greater optimization potential as the coupling relationship pairs were 2.55 compared to 2.32 for Process 1, and the correlation proportions of material systems reached 69.26 % and 56.92 %, respectively. In addition, assessment results indicated that Process 2 was more environmentally friendly because of the lower ecological indexes (9.7 GPt vs. 15.8 GPt) and weaker carbon footprint (CF) (1.16E+08 vs. 2.32E+08). Combined coupling nexus and environmental assessment organically, methanol had the most optimization potential and was beneficial for the measures such as solvent substitution. This work offered theory and practice guidance with demonstrative value to support the sustainable development of precise system management.

Revealing the Excellent Low-Temperature Activity of the Fe1-xCexOδ-S Catalyst for NH3-SCR: Improvement of the Lattice Oxygen Mobility.

The development of selective catalytic reduction catalysts by NH3(NH3-SCR) with excellent low-temperature activity and a wide temperature window is highly demanded but is still very challenging for the elimination of NOx emission from vehicle exhaust. Herein, a series of sulfated modified iron-cerium composite oxide Fe1-xCexOδ-S catalysts were synthesized. Among them, the Fe0.79Ce0.21Oδ-S catalyst achieved the highest NOx conversion of more than 80% at temperatures of 175-375 °C under a gas hourly space velocity of 100000 h-1. Sulfation formed a large amount of sulfate on the surface of the catalyst and provided rich Brønsted acid sites, thus enhancing its NH3 adsorption capacity and improving the overall NOx conversion efficiency. The introduction of Ce is the main determining factor in regulating the low-temperature activity of the catalyst by modulating its redox ability. Further investigation found that there is a strong interaction between Fe and Ce, which changed the electron density around the Fe ions in the Fe0.79Ce0.21Oδ-S catalyst. This weakened the strength of the Fe-O bond and improved the lattice oxygen mobility of the catalyst. During the reaction, the iron-cerium composite oxide catalyst showed higher surface lattice oxygen activity and a faster replenishment rate of bulk lattice oxygen. This significantly improved the adsorption and activation of NOx species and the activation of NH3 species on the catalyst surface, thus leading to the superior low-temperature activity of the catalyst.