Pyrolytic homogeneity enhancement of municipal solid waste using a clustering-based sorting strategy.

Heterogeneity of pyrolytic parameters in municipal solid waste (MSW) significantly hinders its waste-to-energy efficiency. So far, hardly any light has been shed on current pyrolytic heterogeneity conditions or feasible pyrolytic homogeneity enhancement approaches of MSW. Accordingly, pyrolytic properties (Ea and logA) of 130 MSW samples in 6 categories were collected from literature. A kinetic parameters clustering-based sorting strategy for MSW was proposed. A so-called C index was established to compare their sorting performance for Ea and logA against two traditional sorting strategies (substance categorization and density clustering). Results showed that the proposed sorting strategies outperformed the traditional ones in pyrolytic homogeneity enhancement, where the optimal C_Ea and C_logA reached 1578.30 kJ/mol and 93.11 -log min. Among investigated clustering methods, k-means clustering outperformed hierarchical clustering, which could be attributed to its adaptability to the sample structure. Future perspectives involving data set expansion, model framework development, and downstream technologies matching were also discussed. The index C established in this study can be used to evaluate other clustering models.

Application of waste derived magnetic acid-base bifunctional CoFe/biochar/CaO as an efficient catalyst for biodiesel production from waste cooking oil.

The loss of active components, weak acid resistance, and low recover efficiency of common Ca-based catalysts limited its further development and application. In this study, to effectively produce biodiesel from waste cooking oil (WCO), a green and recyclable magnetic acid-base bifunctional CoFe/biochar/CaO catalyst was prepared from sargassum and river snail shell waste via hydrothermal method. The catalysts' structure and properties were investigated by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), CO2/NH3 temperature programmed desorption (CO2/NH3 TPD), etc., The prepared catalyst mainly consisted of the carbon skeleton, CoFe alloy, and CaO. CoFe alloy provided catalyst's ferromagnetism for magnetic separation as well as acid sites for transesterification of WCO. Ca and other metal species with nanoscale (∼5.64 nm) were dispersively anchored on sargassum biochar surface, thereby leading to good catalytic activity (99.21% biodiesel yield) and stability (91.70% biodiesel yield after the 5th cycle). In addition, response surface methodology-Box-Behnken design (RSM-BBD) revealed the optimal operational conditions were 16:1 methanol/oil molar ratio, 3 wt% catalyst dosage, 73 °C for 157 min. The maximum biodiesel yield predicted value was 98.29% and the experimental value was 99.21%, indicating good satisfaction of the established model. Moreover, the quality of WCO biodiesel met the ASTM D6751 standards. This study benefits magnetic waste-derived acid-base bifunctional catalysts for the disposal of WCO towards sustainable biodiesel production.

Effect of landfilling time on physico-chemical properties of combustible fractions in excavated waste.

Excavated waste is a byproduct of microbial decomposition and fermentation following landfill disposal. The effective management and utilization of excavated waste offer broad prospects for environmental and resource protection, as well as economic growth. While current research predominantly focuses on plastics in landfills, the physico-chemical properties of excavated waste over extended landfilling time remain unclear. This study aimed to address this gap by excavating waste from a landfill in Tianjin, China, with a maximum landfilling time of 18 years. The findings revealed that, compared to municipal solid waste (MSW), the excavated waste exhibited increased calorific value, ash content, and fixed carbon content after screening the landfill-mined-soil-like-fine fraction. The average calorific value of the excavated waste could reach 57.8 MJ/kg. Additionally, the oxygen content in the excavated combustible waste exceeded that of MSW, increasing from 25.59 % to 34.22 %. This phenomenon is potentially linked to the oxidation of attached soil impurities and waste. The study identified polyethylene (PE), polypropylene (PP), expanded polystyrene (EPS), polyethylene terephthalate (PET), and wood as the primary combustible components. Notably, the excavated waste exhibited a significant decrease in surface gloss, adopting a rough texture with apparent holes, potentially attributed to the acidification and corrosion of organic matter during fermentation. Nevertheless, the breaking of molecular bonds could also contribute to waste fragmentation. Furthermore, an increase in landfilling time resulted in a more pronounced decrease in mechanical properties. For instance, the failure load of PE decreased from 15.61 N to 6.46 N, and PET reduced from 884.83 N to 186.56 N. The chemical composition of excavated waste has changed, with -OH and CO observed in PE with an 18-year landfilling time. In conclusion, these results provide a theoretical foundation for the recycling of excavated waste and contribute to the advancement of waste management and recycling technologies.

Emerging Strategies for Enhancing Propionate Conversion in Anaerobic Digestion: A Review.

Anaerobic digestion (AD) is a triple-benefit biotechnology for organic waste treatment, renewable production, and carbon emission reduction. In the process of anaerobic digestion, pH, temperature, organic load, ammonia nitrogen, VFAs, and other factors affect fermentation efficiency and stability. The balance between the generation and consumption of volatile fatty acids (VFAs) in the anaerobic digestion process is the key to stable AD operation. However, the accumulation of VFAs frequently occurs, especially propionate, because its oxidation has the highest Gibbs free energy when compared to other VFAs. In order to solve this problem, some strategies, including buffering addition, suspension of feeding, decreased organic loading rate, and so on, have been proposed. Emerging methods, such as bioaugmentation, supplementary trace elements, the addition of electronic receptors, conductive materials, and the degasification of dissolved hydrogen, have been recently researched, presenting promising results. But the efficacy of these methods still requires further studies and tests regarding full-scale application. The main objective of this paper is to provide a comprehensive review of the mechanisms of propionate generation, the metabolic pathways and the influencing factors during the AD process, and the recent literature regarding the experimental research related to the efficacy of various strategies for enhancing propionate biodegradation. In addition, the issues that must be addressed in the future and the focus of future research are identified, and the potential directions for future development are predicted.

Prediction of NH3 and HCN yield from biomass fast pyrolysis: Machine learning modeling and evaluation.

Rapid pyrolysis is a promising technique to convert biomass into fuel oil, where NOX emission remains a substantial environmental risk. NH3 and HCN are top precursors for NOX emission. In order to clarify their migration path and provide appropriate strategies for their controlling, six up-to-date machine learning (ML) models were established to predict the NH3 and HCN yield during rapid pyrolysis of 26 biomass feedstocks. Cross-validation and grid search methods were used to determine the optimal hyperparameters for these ML models. The support vector regression (SVR) model achieved optimal accuracy among them. The optimal root means square error (%), mean absolute error (%), and R2 of test set for NH3/HCN yield were 1.2901/1.1531, 1.0501/0.84712, and 0.98253/0.96152, respectively. In addition, based on the results of Pearson correlation analysis, the input variables with a weak linear correlation with the target product were eliminated, which was found capable of improving the prediction accuracy of almost all ML models except SVR. While after input variables elimination, the SVR model still showed the optimal NH3 and HCN yield prediction accuracy. It reflects SVR's great significance and potential for predicting the yield of NOX precursors during rapid biomass pyrolysis.

Distribution, migration, and removal of N-containing products during polyurethane pyrolysis: A review.

Due to the wide applications of polyurethane (PU), production is constantly increasing, accounting for 8% of produced plastics. PU has been regarded as the 6th most used polymer in the world. Improper disposal of waste PU will result in serious environmental consequences. The pyrolysis of polymers is one of the most commonly used disposal methods, but PU pyrolysis easily produces toxic and harmful nitrogen-containing substances due to its high nitrogen content. This paper reviews the decomposition pathways, kinetic characteristics, and migration of N-element by product distribution during PU pyrolysis. PU ester bonds break to produce isocyanates and alcohols or decarboxylate to produce primary amines, which are then further decomposed to MDI, MAI, and MDA. The nitrogenous products, including NH3, HCN, and benzene derivatives, are released by the breakage of C-C and C-N bonds. The N-element migration mechanism is concluded. Meanwhile, this paper reviews the removal of gaseous pollution from PU pyrolysis and discusses the removal mechanism in depth. Among the catalysts for pollutant removal, CaO has the most superior catalytic performance and can convert fuel-N to N2 by adsorption and dehydrogenation reactions. At the end of the review, new challenges for the utilization and high-quality recycling of PU are presented.

Duodenum-preserving pancreatic head resection compared to pancreaticoduodenectomy: A systematic review and network meta-analysis of surgical outcomes.

Objectives: In this systemic review and network meta-analysis, we investigated pancreaticoduodenectomy (PD), pylorus-preserving pancreaticoduodenectomy (PPPD), and different modifications of duodenum-preserving pancreatic head resection (DPPHR) to evaluate the efficacy of different surgical procedures. Methods: A systemic search of six databases was conducted to identify studies comparing PD, PPPD, and DPPHR for treating pancreatic head benign and low-grade malignant lesions. Meta-analyses and network meta-analyses were performed to compare different surgical procedures. Results: A total of 44 studies were enrolled in the final synthesis. Three categories of a total of 29 indexes were investigated. The DPPHR group had better working ability, physical status, less loss of body weight, and less postoperative discomfort than the Whipple group, while both groups had no differences in quality of life (QoL), pain scale scores, and other 11 indexes. Network meta-analysis of a single procedure found that DPPHR had a larger probability of best performance in seven of eight analyzed indexes than PD or PPPD. Conclusion: DPPHR and PD/PPPD have equal effects on improving QoL and pain relief, while PD/PPPD has more severe symptoms and more complications after surgery. PD, PPPD, and DPPHR procedures exhibit different strengths in treating pancreatic head benign and low-grade malignant lesions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42022342427.

Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning.

Hydrothermal biochar is a promising sustainable soil remediation agent for plant growth. Demands for biochar properties differ due to the diversity of soil environment. In order to achieve accurate biochar properties prediction and overcome the interpretability bottleneck of machine learning models, this study established a series of data-enhanced machine learning models and conducted relevant sensitivity analysis. Compared with traditional support vector machine, artificial neural network, and random forest models, the accuracy after data enhancement increased in average from 5.8% to 15.8%, where the optimal random forest model showed the average of accuracy was 94.89%. According to sensitivity analysis results, the essential factors influencing the predicting results of the models were reaction temperature, reaction pressure, and specific element of biomass feedstock. As a result, data-enhanced interpretable machine learning proved promising for the characteristics prediction of hydrothermal biochar.

Complete mitochondrial DNA sequence of Tupaia belangeri yaoshanensis (Wang, 1987) from Dayao Mountains in China.

The tree shrew (Tupaia belangeri) is currently placed in the order Scandentia. Owing to their unique characteristics, such as small body size, high brain-to-body mass ratio, short reproductive cycle and life span, and low maintenance costs in laboratory conditions, tree shrews have been proposed as alternative experimental animals to primates in biomedical research. In this study, we determined the complete mitochondrial genome of the subspecies Tupaia belangeri yaoshanensis (T.b. yaoshanensis). The mitochondrial DNA (mtDNA) is 16,777 bp long and contains 13 protein-coding genes (PCGs), two ribosomal RNA genes (12S and 16S), and 22 transfer RNA (tRNA) genes. The base composition of the mitogenome was A (32.28%), T (26.82%), G (14.79%), and C (26.11%). For the 13 PCGs, 1405 variable sites were found between T.b. yaoshanensis and T.b. chinensis (JN800724), of which 916 were synonymous and 489 were nonsynonymous. The frequency of mutations significantly varied among the different genes, with the highest value in the mt-NAD5 gene of tree shrews. Phylogenetic analysis based on the amino acid sequences of 13 PCGs revealed a closer relationship between the species of Scandentia and Lagomorpha. To the best of our knowledge, this is the first study to report the complete mitochondrial genome sequence of T.b. yaoshanensis.

Association Between Gastric Antral Cross-sectional Area and Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Cholecystectomy.

OBJECTIVE: To explore the predictive value of gastric antral cross-sectional area (CSA) in the occurrence of postoperative nausea and vomiting (PONV) by analysing the association between gastric antral CSA measured by ultrasound and frequency of PONV after laparoscopic cholecystectomy. STUDY DESIGN: Observational study. Place and Duration of the Study: Department of Anaesthesiology, The First Hospital of Wannan Medical College, Yijishan Hospital of Wannan Medical College, from October 2021 to February 2022. METHODOLOGY: Gastric antral ultrasound (US) was performed in 266 patients undergoing laparoscopic cholecystectomy (LC) before anaesthesia induction, after it, and after surgery. The data obtained were used to evaluate the relationship between gastric antral CSA and PONV. RESULTS: The gastric antral CSA in Semi-recumbent decubitus (SRD) position >398.85 mm2 (AUC=0.623) highly indicated the occurrence of PONV. In addition, the subject performance characteristic curve of a binary logistics retrospective model for predicting PONV (AUC=0.805) highly indicated the occurrence of PONV. CONCLUSION: Gastric US assessment of gastric antral CSA might change the current assessment model of PONV risk. This study showed that the gastric antral CSA in the SRD position after anaesthesia induction and a binary logistics retrospective model could be used to predict the occurrence of PONV, which could be helpful to adjust intraoperative and postoperative interventions and accelerate the recovery of patients. KEY WORDS: Postoperative nausea and vomiting, Gastric ultrasound, Gastric cross-sectional area, Perioperative period.

Graphene quantum dots (GQDs) induce thigmotactic effect in zebrafish larvae via modulating key genes and metabolites related to synaptic plasticity.

Graphene quantum dots (GQDs) recently gain much attention for its medicinal values in treating diseases such as neurodegeneration and inflammations. However, owing to the high permeability of GQDs across the blood-brain barrier, whether its retention in the central nervous system (CNS) perturbs neurobehaviors remains less reported. In the study, the locomotion of zebrafish larvae (Danio rerio) was fully evaluated when administrated by two GQDs in a concentration gradient, respectively as reduced-GQDs (R-GQDs): 150, 300, 600, 1200, and 2400 g/L, and graphene oxide QDs (GOQDs): 60, 120, 240, 480, and 960 g/L. After exposure, the larvae were kept for locomotion analysis within one week's depuration. Substantial data showed that the basal locomotor activity of zebrafish larvae was not significantly changed by both two GQDs at low concentrations while weakened greatly with the increase of concentrations, and the total ATP levels of zebrafish larvae were also found to decrease significantly when exposed to the highest concentrations of GQDs. Next, the thigmotactic effect was observed to be remarkably induced in larvae by both two GQDs at any concentrations during exposure, and remained strong in larvae treated by high concentrations of R-GQDs after 7 days' depuration. To be noted, we found that GQDs affected the synaptic plasticity via downregulating the mRNA levels of NMDA and AMPA receptor family members as well as the total glutamine levels in zebrafish larvae. Together, our study presented robust data underlying the locomotor abnormalities aroused by GQDs in zebrafish larvae and indicated the potential adverse effects of GQDs on synaptic plasticity.

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste.

The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to less satisfactory recognition for its reliability. Accordingly, this paper aimed to explore the chemical insights of the machine learning models in the fast characterization process. A novel dimensional reduction method with significant physicochemical meanings was thus proposed, where the high loading spectral peaks of BW were selected as input features. Combined with functional groups attribution of these spectral peaks, the machine learning models established based on the dimensionally reduced spectral data could be explained with clear chemical insights. The performance of classification and regression models between the proposed dimensional reduction method and principal component analysis method was compared. The influence mechanism of each functional group on the characterization results were discussed. CH deformation, CC stretch & CO stretch and ketone/aldehyde CO stretch played essential roles in C, H/ LHV and O prediction, respectively. The results of this work demonstrated the theoretical fundamentals of the machine learning and spectroscopy based BW fast characterization method.

Simulation of integrated anaerobic digestion-gasification systems using machine learning models.

In this study, the anaerobic digestion model M-ADM1 was integrated with the gasification model T-ANN to form a set of integrated models that can efficiently simulate the biomass AD-GS integration technology. Biogas slurry is used as feedstocks to prepare biogas slurry fertilizer. Solid residue is used feedstocks for gasification reactions. Biogas and syngas from the gasification of solid residue are used for energy. In this process, carbon emission is regarded as an important index for the comprehensive evaluation and optimization of AD-GS integration process. This study found that when the anaerobic digestion duration was 0 to 15 days, the carbon emission reduction increased rapidly. The amount of carbon emission reduction peaks on day 15. The value of carbon emission reduction is 0.1828 gCO2eq. In addition, when FEAG reached the maximum value at 15 days of anaerobic digestion, the decreasing trend of FEAG rate change value started to become significant.

Emergy analysis of agricultural waste biomass for energy-oriented utilization in China: Current situation and perspectives.

Agricultural waste biomass (AWB) is becoming a significant sustainable alternative for fossil fuels. Emergy analysis (EmA) is a promising methodology that provides a uniform standard to assess simultaneously the environmental load and economic returns of a system. Relevant studies on the assessment of AWB energy-oriented utilization by EmA are attracting researchers' attention worldwide. Therefore, this paper aimed to comprehensively review state-of-the-art applications of the EmA for AWB energy-oriented utilization systems. Results indicated that there were limitations and challenges in the application of single EmA. Importantly, the boundary of AWB energy-oriented utilization systems in the application of EmA was not unified, leading to poor comparability of the impact results. Although the effect of policies has a significant influence on the application and promotion of AWB energy-oriented utilization, the EmA method can hardly reflect the effect of policies. Therefore, there is a need in combination with other methods to optimize the EmA, thus providing comprehensive guidance for decision-makers. Finally, based on these, some feasible suggestions especially to (1) further promote the application and (2) development of this research field were presented. It is hoped that this work could support the proper evaluation and further optimization of AWB energy-oriented utilization systems.

Hydrothermal oxygen uncoupling of high-concentration biogas slurry over Cu-α-Fe2O3·α-MoO3 catalyst.

A hydrothermal oxygen uncoupling (HTOU) method which combines aqueous phase reforming (APR) and oxygen uncoupling was proposed to treat biogas slurry (BS). Based on Le Chatelier's principle, this novel approach was constructed and realized by Cu-α-Fe2O3·α-MoO3 catalyst with van der Waals heterojunction-redox property. Additionally, the catalyst was synthesized by integrating a simple one-pot sol-gel method and thermal hydrogenating. Results indicated that the optimal removal efficiencies of non-purgeable organic carbon (NPOC) (76.29%), total nitrogen (TN) (45.56%), and ammonia nitrogen (AN) (29.03%) were achieved on the Cu-α-Fe2O3·α-MoO3 catalyst at 225.00 °C for 30.00 min, respectively. The significant performance of Cu-α-Fe2O3·α-MoO3 could be attributed to three aspects. (1) The α-MoO3 nanosheets with van der Waals heterostructures obtained at the calcination temperature of 600.00 °C, which can provide the superior performance of APR for hydrogen generation. (2) The adsorbed oxygen species were eliminated by thermal hydrogenating which had a surface passivation effect. (3) The effect of oxygen uncoupling in the lattice oxygen and gaseous oxygen release reaction was beneficial to the degradation of organic matter. Moreover, the reuse of catalysts studies further revealed that the deactivation of catalysts originated from carbon deposition of aromatic polymers and heavy metals oxides pollution. Overall, these findings disclosed that the HTOU could be a promising alternative to the treatment of high-concentration organic wastewater.

Pyrolysis properties and kinetics of photocured waste from photopolymerization-based 3D printing: A TG-FTIR/GC-MS study.

The emerging photopolymerization-based 3D printing industry has led to a growing concern for the disposal of photocured waste (PCW), which is inevitably generated during the life cycle of photopolymerization-based 3D printing. In order to shed light on suitable thermochemical treatment and utilization approaches of PCW, this work comprehensively investigated the properties and kinetics during PCW pyrolysis via TG-FTIR/GC-MS analysis. The results demonstrated that the main decomposition of PCW sample happened in the range 320-550 °C with a total weight loss of 93.34 wt%. According to the result of four kinetic models, the activation energy of PCW sample was approximately 228.58-245.05 kJ/mol. Finally, the FTIR and GC-MS results manifested that the main components of volatiles released at different heating rates were the same. The volatiles mainly include (S)-(+)-2-hydroxy-2-phenylprop, benzaldehyde, benzophenone (photo-initiator), benzoic acid, benzoylformic acid etc., which have a multitude of potential applications. However, these volatiles produced by PCW pyrolysis have a certain toxicity and potential hazard. This study demonstrates insightful fundamentals for thermochemical disposal of PCW, which appears to be potentially valuable with the rapid development of the photopolymerization-based 3D printing industry.

Numerical Analysis of Droplet Impact on the Convex Surface with Liquid Film.

Droplet impact on film is a common natural appearance in industrial production. The numerical simulation method is used to simulate the evolution process of droplet impact on different convex surfaces with liquid film to study the influence of various surface structures on the impact of droplets on the film. The mechanism of droplet impact on irregular wall is also explored. The effect of the height and width of the convex on the evolution of the interphase interface and the characteristic parameters of the crown and cavity are discussed. Based on the results, the convex has a great effect on the change form of droplets after impacting the liquid film. A region of high pressure will appear above the convex, and vortices will form around it. In terms of the characteristics of the crown, the growth rate of the crown height increases with the height of the convex. The spreading diameter of the crown increases with the height of the convex. The effect of convex width on the crown is less than that of convex height. The convex has a great influence on the evolution of the cavity, and the convex of different scales will affect the development shape of the cavity. The present finding provides theoretical guidance for the application of droplets impinging on liquid film.

Evolution of research topics on the Tibetan Plateau environment and ecology from 2000 to 2020: a paper mining.

The Tibetan Plateau works as an important environmental and ecological barrier for the Asian continent. The researches on Tibetan environment and ecology are becoming extensive, but there is no systematic summary of research hotspots and trends in this field. Here, we analyzed 9180 publications retrieved from the WOS and CNKI during 2000 ~ 2020. The characteristics of publication, keywords with a 5-year interval and co-occurrence analysis were carried out so as to reveal the evolution and development trends of topics. The results show that articles increase dramatically since 2012. Except for common concerns like evolution, climate change, and precipitation, diversity was been studied more by Chinese scholars, while the USA, Germany, UK, and Australia researchers focused more on adaptability, basin western Tibet, lake, barley, and ore-related themes, respectively. Besides, China closely collaborated with the USA, Australia, and Germany in topics of evolution, climate change and degradation, precipitation, and diversity. The institutions located in Chinese different economic regions focused on different research keywords, such as vegetation, growth, trace elements, and geochemistry. The CAS contributed the most articles with 4254, showed advantages both in quantity and quality. Few articles were published by researchers affiliated to Free University of Berlin but with higher citations. It is the only one institute outside of China in the top 20. Main research hotspots include climate change, geology, and diversity. In future researches, ecological management and rehabilitation of mining area and tailings ponds, waste disposal, and changes of soil and water quality are worthy of attention and funding.

Baicalin-liposomes loaded polyvinyl alcohol-chitosan electrospinning nanofibrous films: Characterization, antibacterial properties and preservation effects on mushrooms.

To investigate antibacterial properties and application in food preservation of nanofibrous films (NFs), baicalin-liposomes (BCL-LPs) were loaded into polyvinyl alcohol-chitosan (PVA-CS) substrates to form NFs using electrospinning technology. The microstructure and phase identification of the NFs were characterized. The antibacterial properties and cytotoxicity of NFs were determined. The preservation of the NFs to mushrooms was evaluated. The results showed that smooth and uniform NFs were formed through molecular interaction between BCL-LPs and PVA-CS matrix. The NFs exhibited good antibacterial effects on Escherichia coli and Staphylococcus aureus due to the bacterial destruction resulting from the BCL delivery to bacterial cells by liposomes. In addition, the NFs were compatible with L929 fibroblasts. The BCL-LPs/PVA-CS NFs inhibited weight loss, browning, rancidity and bacterial growth as well as maintained the nutrients of mushrooms. The results show BCL-LPs/PVA-CS NFs possessed effective antibacterial properties, non-cytotoxicity and preservation performance, indicating the potential utilization as food-active packing.