Alkaline absorbents for SO2 and SO3 removal: A comprehensive review.

Injection of an alkaline absorbent into the flue gas can significantly reduce SO2 and SO3 emissions. The article presents alkaline absorbents employed in industrial processes to remove SO2 and SO3 from flue gases, detailing their characteristics and applications across various process conditions. It summarizes the mechanisms and influencing factors behind SO2 and SO3 removal, outlines the impact of multi-component gases, particularly SO2, on SO3 removal in actual flue gases, and elucidates this competitive phenomenon from a theoretical standpoint. The article compares the application scenarios and efficiencies of alkaline absorbents across different processes, identifies the optimal combinations of various absorbents and processes, and proposes a synergistic approach for the removal of SO2 and SO3. The findings demonstrate that by injecting calcium- or sodium-based absorbents into dry processes, SO2 and SO3 can be removed efficiently and cost-effectively, with process optimization and absorbent modifications further enhancing the SOx removal efficiency. In the future, by blending two or more absorbents and applying them to dry processes, a synergistic removal of SO2 and SO3 can be achieved.

Abrogating PDK4 activates autophagy-dependent ferroptosis in breast cancer via ASK1/JNK pathway.

BACKGROUND: Targeting ferroptosis mediated by autophagy presents a novel therapeutic approach to breast cancer, a mortal neoplasm on the global scale. Pyruvate dehydrogenase kinase isozyme 4 (PDK4) has been denoted as a determinant of breast cancer metabolism. The target of this study was to untangle the functional mechanism of PDK4 in ferroptosis dependent on autophagy in breast cancer. METHODS: RT-qPCR and western blotting examined PDK4 mRNA and protein levels in breast cancer cells. Immunofluorescence staining appraised light chain 3 (LC3) expression. Fe (2 +) assay estimated total iron level. Relevant assay kits and C11-BODIPY (591/581) staining evaluated lipid peroxidation level. DCFH-DA staining assayed intracellular reactive oxygen species (ROS) content. Western blotting analyzed the protein levels of autophagy, ferroptosis and apoptosis-signal-regulating kinase 1 (ASK1)/c-Jun N-terminal kinase (JNK) pathway-associated proteins. RESULTS: PDK4 was highly expressed in breast cancer cells. Knockdown of PDK4 induced the autophagy of breast cancer cells and 3-methyladenine (3-MA), an autophagy inhibitor, countervailed the promoting role of PDK4 interference in ferroptosis in breast cancer cells. Furthermore, PDK4 knockdown activated ASK1/JNK pathway and ASK1 inhibitor (GS-4997) partially abrogated the impacts of PDK4 absence on the autophagy and ferroptosis in breast cancer cells. CONCLUSION: To sum up, deficiency of PDK4 activated ASK1/JNK pathway to stimulate autophagy-dependent ferroptosis in breast cancer.

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy.

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.

Phase Change Study of a New Two-Phase Absorbent Based on DAP.

In order to overcome the drawbacks of conventional absorbents, which exhibit slow absorption rates and low absorption loads, this study suggests enhancing the absorbent system for CO2 absorption by incorporating a nonaqueous solvent into 1,3-propanediamine (DAP) and tetramethylethylenediamine (TMEDA), resulting in a two-phase system. The mechanism of solvent absorption of CO2 was investigated using nuclear magnetic resonance (NMR) carbon spectroscopy. By comparing the absorption load, fraction ratio, and viscosity of different absorbents after absorbing carbon dioxide, the two-phase absorbents with good performance were selected. The poor water absorbent consisting of the DAP/TMEDA system exhibited an absorption load of 3.8 mol/kg, surpassing that of the conventional 30% ethanolamine solution. A nonaqueous solvent is added to the system to replace some of the water to reduce the fraction. After adding different nonaqueous solvents, the phase separation system was screened after 2 h of CO2 absorption. The system with good performance was tested for the absorption of the solution under different amine concentration and water concentration tests. It is found that the absorption load of the DAP/TMEDA/diglyme system is 3.2 mol/kg, but the fraction can be reduced to 38%. The significant reduction in rich phase volume is beneficial for reducing the size and cost of regeneration tower. According to NMR detection and quantum chemical calculations, it was found that DAP/TMEDA absorbs carbon dioxide to form carbamate. DAP acts as the main absorbent, while TMEDA and nonaqueous solvents do not participate in the absorption reaction. Nonaqueous solvents were found to accelerate the solution phase separation due to the salt precipitation reaction.

Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review.

PURPOSE: The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty. METHODS: This scoping review followed the PRISMA, PRISMA-ScR guidelines, and five stage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected. RESULTS: Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review. CONCLUSIONS: AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems.

Deep learning to automatically classify very large sets of preoperative and postoperative shoulder arthroplasty radiographs.

BACKGROUND: Joint arthroplasty registries usually lack information on medical imaging owing to the laborious process of observing and recording, as well as the lack of standard methods to transfer the imaging information to the registries, which can limit the investigation of various research questions. Artificial intelligence (AI) algorithms can automate imaging-feature identification with high accuracy and efficiency. With the purpose of enriching shoulder arthroplasty registries with organized imaging information, it was hypothesized that an automated AI algorithm could be developed to classify and organize preoperative and postoperative radiographs from shoulder arthroplasty patients according to laterality, radiographic projection, and implant type. METHODS: This study used a cohort of 2303 shoulder radiographs from 1724 shoulder arthroplasty patients. Two observers manually labeled all radiographs according to (1) laterality (left or right), (2) projection (anteroposterior, axillary, or lateral), and (3) whether the radiograph was a preoperative radiograph or showed an anatomic total shoulder arthroplasty or a reverse shoulder arthroplasty. All these labeled radiographs were randomly split into developmental and testing sets at the patient level and based on stratification. By use of 10-fold cross-validation, a 3-task deep-learning algorithm was trained on the developmental set to classify the 3 aforementioned characteristics. The trained algorithm was then evaluated on the testing set using quantitative metrics and visual evaluation techniques. RESULTS: The trained algorithm perfectly classified laterality (F1 scores [harmonic mean values of precision and sensitivity] of 100% on the testing set). When classifying the imaging projection, the algorithm achieved F1 scores of 99.2%, 100%, and 100% on anteroposterior, axillary, and lateral views, respectively. When classifying the implant type, the model achieved F1 scores of 100%, 95.2%, and 100% on preoperative radiographs, anatomic total shoulder arthroplasty radiographs, and reverse shoulder arthroplasty radiographs, respectively. Visual evaluation using integrated maps showed that the algorithm focused on the relevant patient body and prosthesis parts for classification. It took the algorithm 20.3 seconds to analyze 502 images. CONCLUSIONS: We developed an efficient, accurate, and reliable AI algorithm to automatically identify key imaging features of laterality, imaging view, and implant type in shoulder radiographs. This algorithm represents the first step to automatically classify and organize shoulder radiographs on a large scale in very little time, which will profoundly enrich shoulder arthroplasty registries.

Deep Learning Artificial Intelligence Tool for Automated Radiographic Determination of Posterior Tibial Slope in Patients With ACL Injury.

Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. Purpose/Hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. Results: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. Conclusion: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next.

Background: Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications. Methods: PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery. Results: Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology. Conclusion: AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.

Few-shot biomedical image segmentation using diffusion models: Beyond image generation.

BACKGROUND: Medical image analysis pipelines often involve segmentation, which requires a large amount of annotated training data, which is time-consuming and costly. To address this issue, we proposed leveraging generative models to achieve few-shot image segmentation. METHODS: We trained a denoising diffusion probabilistic model (DDPM) on 480,407 pelvis radiographs to generate 256 âœ• 256 px synthetic images. The DDPM was conditioned on demographic and radiologic characteristics and was rigorously validated by domain experts and objective image quality metrics (Frechet inception distance [FID] and inception score [IS]). For the next step, three landmarks (greater trochanter [GT], lesser trochanter [LT], and obturator foramen [OF]) were annotated on 45 real-patient radiographs; 25 for training and 20 for testing. To extract features, each image was passed through the pre-trained DDPM at three timesteps and for each pass, features from specific blocks were extracted. The features were concatenated with the real image to form an image with 4225 channels. The feature-set was broken into random patches, which were fed to a U-Net. Dice Similarity Coefficient (DSC) was used to compare the performance with a vanilla U-Net trained on radiographs. RESULTS: Expert accuracy was 57.5 % in determining real versus generated images, while the model reached an FID = 7.2 and IS = 210. The segmentation UNet trained on the 20 feature-sets achieved a DSC of 0.90, 0.84, and 0.61 for OF, GT, and LT segmentation, respectively, which was at least 0.30 points higher than the naively trained model. CONCLUSION: We demonstrated the applicability of DDPMs as feature extractors, facilitating medical image segmentation with few annotated samples.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence.

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.

Mechanistic insights into the leaching and environmental safety of arsenic in ceramsite prepared from fly ash.

Utilizing fly ash to prepare ceramsite is a promising way to immobilize heavy metals and recycle industrial solid waste. However, traditional preparation method of fly ash ceramsite has the disadvantages of large ignition loss. Therefore, the present study applied the pressure molding method to enhance solid content and improve the strength of ceramsite. The optimal preparation conditions of ceramsite were suggested as preheating at 450 °C for 25 min followed by sintering at 1050 °C for 30 min. Under such conditions, ceramsite with high compressive strength of 10.8 Mpa, bulk density of 878 kg m-3, and 1-h water absorption of 18.5% was fabricated, in compliance with Chinese standard (GB/T 1743.1-2010). The arsenic leaching concentration from the resulting product was considerably lower than Chinese standard (GB 5085.3-2007). Moreover, arsenic volatilization during ceramsite calcination was insignificant, and the vast majority of arsenic remained in resulting ceramsite. A geochemical speciation model developed for the multiple component system in ceramsite suggested that FeAsO4, Ca5(OH) (AsO4)3, and hydrous ferric oxide adsorption are the primary mechanisms retaining arsenic in ceramsite. Additionally, based on density functional theory calculations and biotoxicity test, the binding site of arsenic atom on mineral components and the environmental safety of ceramsite was determined and evaluated.

A nano-conductive osteogenic hydrogel to locally promote calcium influx for electro-inspired bone defect regeneration.

Conductive nano-materials and electrical stimulation (ES) have been recognized as a synergetic therapy for ordinary excitable tissue repair. It is worth noting that hard tissues, such as bone tissue, possess bioelectrical properties as well. However, insufficient attention is paid to the synergetic therapy for bone defect regeneration via conductive biomaterials with ES. Here, a novel nano-conductive hydrogel comprising calcium phosphate-PEDOT:PSS-magnesium titanate-methacrylated alginate (CPM@MA) was synthesized for electro-inspired bone tissue regeneration. The nano-conductive CPM@MA hydrogel has demonstrated excellent electroactivity, biocompatibility, and osteoinductivity. Additionally, it has the potential to enhance cellular functionality by increasing endogenous transforming growth factor-beta1 (TGF-ß1) and activating TGF-ß/Smad2 signaling pathway. The synergetic therapy could facilitate intracellular calcium enrichment, resulting in a 5.8-fold increase in calcium concentration compared to the control group in the CPM@MA ES + group. The nano-conductive CPM@MA hydrogel with ES could significantly promote electro-inspired bone defect regeneration in vivo, uniquely allowing a full repair of rat femoral defect within 4 weeks histologically and mechanically. These results demonstrate that our synergistic strategy effectively promotes bone restoration, thereby offering potential advancements in the field of electro-inspired hard tissue regeneration using novel nano-materials with ES.

Competitive adsorption characteristics of VOCs and water vapor by activated carbon prepared from Fe/N-doped pistachio shell.

Various materials have been developed to capture volatile organic compounds (VOCs) to mitigate air pollution. However, sorbent materials with excellent resistance to water are rare. Here, several Fe/N-doped activated carbons (ACs) have been prepared to capture VOCs in humid environments. The ACs were analyzed by various characterization techniques, such as BET, SEM, XPS, XRD, FTIR, and Raman. The results showed that Fe/N doping resulted in the specific surface area of the ACs increasing by 500 to 1000 m2 g-1, the average pore size increasing to approximately 2 nm, improved mesoporous structure, higher graphitization, lower hydrophilicity, and polarity. The VOCs adsorption performance of the ACs was evaluated by static and dynamic adsorption experiments. The uptake of toluene and ethyl acetate by ACs was enhanced to 224 mg g-1 and 135 mg g-1, respectively. And ACs were able to maintain 70 to 80% VOCs adsorption capacity for VOCs at 80% relative humidity. Furthermore, the microscopic mechanisms were investigated by the grand canonical Monte Carlo method (GCMC). The highly graphitized structure and the N functional groups favored the VOC adsorption process and discouraged the adsorption of water vapor. This work affirmed the dominance of Fe/N-doped carbon, which will contribute to the evolution of water-resistant VOCs adsorbent materials.

Lycorine suppresses the malignancy of breast carcinoma by modulating epithelial mesenchymal transition and ß-catenin signaling.

Lycorine, an isoquinoline alkaloid can exhibit significant anti-cancer effects. The present study was conducted to illustrate the underlying mechanisms of action of lycorine on breast carcinoma under in vitro and in vivo settings Tandem Mass Tag assay and Kyoto Encyclopedia of Genes and Genomes analysis revealed that 20 signaling pathways were closely related to tumorigenesis, especially Wnt signaling pathway and tight junctions. The results demonstrated that lycorine evidently inhibited the proliferation of MDA-MB-231 and MCF-7 cells with IC50 values of 1.84 ± 0.21 µM and 7.76 ± 1.16 µM, respectively. It also blocked cell cycle in G2/M phase, caused a decrease in mitochondrial membrane potential, and induced apoptosis pathways through regulating caspase-3, caspase-8, caspase-9, and PARP expression. Moreover, lycorine effectively repressed the ß-catenin signaling and reversed epithelial-mesenchymal transition (EMT) process. Furthermore, 4T1/Luc homograft tumor model was used to further demonstrate that lycorine significantly inhibited the growth and metastasis of breast tumor. These findings highlight the significance of lycorine as potential anti-neoplastic agent to combat breast cancer.

Berberine inhibits breast carcinoma proliferation and metastasis under hypoxic microenvironment involving gut microbiota and endogenous metabolites.

A potential role of berberine, a benzyl isoquinoline alkaloid, in cancer therapy is apparent. Its underlying mechanisms of berberine against breast carcinoma under hypoxia have not been elucidated. We focused on the doubt how berberine restrains breast carcinoma under hypoxia in vitro and in vivo. A molecular analysis of the microbiome via 16 S rDNA gene sequencing of DNA from mouse faeces confirmed that the abundances and diversity of gut microbiota were significantly altered in 4T1/Luc mice with higher survival rate following berberine treatment. A metabolome analysis liquid chromatography-mass spectrometer/mass spectrometer (LC-MS/MS) revealed that berberine regulated various endogenous metabolites, especially L-palmitoylcarnitine. Furthermore, the cytotoxicity of berberine was investigated in MDA-MB-231, MCF-7, and 4T1 cells. In vitro to simulate under hypoxic environment, MTT assay showed that berberine inhibited the proliferation of MDA-MB-231, MCF-7, and 4T1 cells with IC50 values of 4.14 ± 0.35 µM, 26.53 ± 3.12 µM and 11.62 ± 1.44 µM, respectively. Wound healing and trans-well invasion studies revealed that berberine inhibited the invasion and migration of breast cancer cells. RT-qPCR analysis shed light that berberine reduced the expression of hypoxia-inducible factor-1α (HIF-1α) gene. Immunofluorescence and western blot demonstrated that berberine decreased the expression of E-cadherin and HIF-1α protein. Taken together, these results provide evidence that berberine efficiently suppresses breast carcinoma growth and metastasis in a hypoxic microenvironment, highlighting the potential of berberine as a promising anti-neoplastic agent to combat breast carcinoma.

Synergistic Effect of Magneto-Mechanical Bioengineered Stem Cells and Magnetic Field to Alleviate Osteoporosis.

Therapeutic bioengineering based on stem cell therapy holds great promise in biomedical applications. However, the application of this treatment is limited in orthopedics because of their poor survival, weak localization, and low cell retention. In this work, magneto-mechanical bioengineered cells consisting of magnetic silica nanoparticles (MSNPs) and mesenchymal stem cells (MSCs) are prepared to alleviate osteoporosis. The magneto-mechanical bioengineered MSCs with spatial localization, cell retention, and directional tracking capabilities could be mediated by a guided magnetic field (MF) in vitro and in vivo. Furthermore, high uptake rates of the MSNPs ensure the efficient construction of magnetically controlled MSCs within 2 h. In conjunction with external MF, the magneto-mechanical bioengineered MSCs have the potential for the activation of the YAP/ß-catenin signaling pathway, which could further promote osteogenesis, mineralization, and angiogenesis. The synergistic effects of MSNPs and guided MF could also decline bone resorption to rebalance bone metabolism in bone loss diseases. In vivo experiments confirm that the functional MSCs and guided MF could effectively alleviate postmenopausal osteoporosis, and the bone mass of the treated osteoporotic bones by using the bioengineered cells for 6 weeks is nearly identical to that of the healthy ones. Our results provide a new avenue for osteoporosis management and treatment, which contribute to the future advancement of magneto-mechanical bioengineering and treatment.

The fate of selenium in the desulfurization wastewater evaporation process: Migration mechanisms and risk analysis.

Owing to the implementation of the zero wastewater discharge policy in China, stricter supervision and technical requirements have been imposed. Hot flue gas evaporation technology exhibits significant advantages in desulfurization wastewater treatment. However, volatile constituents (such as selenium, Se) present in wastewater may be released, thus disrupting the power plant's original Se balance. In this study, the evaporation of three desulfurization wastewater plants is performed. The release of Se mainly begins from the threshold at which wastewater is evaporated to dryness, and Se release rates of 21.5, 25.1, and 35.6% are detected. Furthermore, the key components and properties of wastewater for Se migration are identified through experiments and density functional theory calculations. Lower pH values and Cl- contents are not conducive to Se stability, and this tendency is more pronounced in selenite. The suspended solid content temporarily traps the Se in the initial evaporation process, as confirmed via a decrease in the Se release rate and a high binding energy (-307.7 kJ/mol). Moreover, results of risk assessment reveal that wastewater evaporation results in a negligible increase in Se concentration. This study evaluates the risk of Se release during wastewater evaporation and provides a basis for Se emission control strategies.

Concentration of desulfurization wastewater using low-temperature flue gas and associated corrosion risk.

The low-temperature flue gas concentration is an effective pretreatment strategy to realize the zero discharge of desulfurization wastewater. However, the water quality changes during concentration and potential risks are not transparent. In this work, the concentration process is conducted on a lab-scale platform. The results show that the ion concentrations of wastewater does not increase linearly straightforwardly during the experiment, mainly due to the complex interaction between ions. Besides, the average size of precipitated products increases from 8.73 to 23.19 µm, which could be ascribed to the aggregation effect. The microscopic and spectroscopic characterization analysis further confirms this effect. In addition, the potential risk of corrosion is investigated. The decreased pH and the increasing corrosive ions concentration intensified the pitting effect on metals. The concentrated desulfurization wastewater exhibited a strong pitting effect on 20# carbon steel (corrosion rate of 1.2113 mm·a-1.), while 316 stainless steel shows excellent corrosion resistance (corrosion rate of 0.0994 mm·a-1.). This work provides a reference for the low-temperature flue gas concentration technology and would serve as an essential guideline for practical engineering.

Release and migration of Hg during desulfurization wastewater evaporation process: Whole process evaluation by experimental and theoretical study.

Hot flue gas evaporation technology is an effective strategy for zero liquid discharge of desulfurization wastewater. However, there is a potential risk that heavy metals such as Hg may be released from the wastewater during evaporation, disrupting the original balance of the power plant or even exceeding the Hg emission standard. Wastewater evaporation and Hg release behavior were obtained using a single droplet drying system. At an evaporation temperature of 300 °C, approximately 18.5% of Hg was released in the constant wet-bulb temperature period, and the remaining was released in the following evaporation periods. Furthermore, a fixed-bed experiment, in combination with density functional theory calculations, was used to investigate the possible migration mechanisms of released Hg. The results revealed that high HCl concentration, introduced fly ash, and precipitated evaporation products play a crucial role in the fate of Hg, and 85.3% of Hg finally turned into less harmful particulate-bound Hg. This study provides a new and effective strategy for evaluating the migration process of pollutants in wastewater treatment. Moreover, it will serve as an essential reference for advanced wastewater treatment and heavy metals control technologies in the future.