Acid Leaching Extraction Mechanism of Aluminum and Iron Ions from Coal Gangue Based on CaF2 Assistance and Process Optimization.

To reveal how CaF2 improves the dissolution ratios of aluminum and iron ions in coal gangue, CaF2 and hydrochloric acid are used to extract Al3+ and Fe3+ from the coal gangue calcined powder. The leaching ratios of Al3+ and Fe3+ are measured, and the filter residues are analyzed by BET, XRD, and SEM. The results show that adding 3% CaF2 could increase the extraction ratio of Al3+ from 62.96% to 92.10% under optimized conditions, and that of Fe3+ is increased from 85.12% to 95.73%. The mechanism of CaF2 as an auxiliary to improve the leaching ratio of Fe3+ is that HF reacts with the thin layers of gangue calcined powder containing silica to form soluble SiF4, thus forming pores that promote the diffusion of H+ and inner ions, improving the leaching ratios of Al3+ and Fe3+. Finally, the CaF2-assisted acid leaching process is optimized. The results showed that it is efficient and feasible to extract Al3+ and Fe3+ with the assistance of CaF2 and that HF has a catalytic effect in the reaction system. This work provides a reference for the next step of actual production.

Boosting visible light photocatalysis in BiOI/BaFe12O19 magnetic heterojunction.

Many previous studies have underestimated the role of magnetic components in improving photocatalytic performance. It is significance to explore the migration mechanism of photoinduced carriers in magnetic heterojunction. Here, a magnetic heterojunction, BiOI/BaFe12O19, was synthesized by a simple preparation method. The optimal synthesis conditions and photocatalytic reaction conditions were explored. The growth mechanism of bismuth iodide oxide (BiOI) was elaborated by introducing a micromagnetic field stemming from barium ferrite (BaFe12O19). The electrochemical impedance spectroscopy (EIS), Mott-Schottky curve (MS), transient fluorescence spectrometer (PL), and photocurrent response plot (i ~ t) tests indicated that the BiOI/BaFe12O19 possessed a higher transfer capacity of electrons, higher separation efficiency of photoinduced carriers, stronger photocurrent response, and higher carriers density, compared with pure BiOI. The ultraviolet-visible diffuse reflectance spectrophotometer (UV-vis DRS), electron paramagnetic resonance spectrometer (EPR), MS, and quenching experiments revealed band structure configuration and migration mechanism of photoinduced carriers. The enhancement mechanism of photocatalysis and photocatalytic reaction mechanism was clearly proclaimed in BiOI/BaFe12O19 catalytic system.

Correction: Kong et al. Preparation of Poly Aluminum-Ferric Chloride (PAFC) Coagulant by Extracting Aluminum and Iron Ions from High Iron Content Coal Gangue. Materials 2022, 15, 2253.

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Preparation of Poly Aluminum-Ferric Chloride (PAFC) Coagulant by Extracting Aluminum and Iron Ions from High Iron Content Coal Gangue.

Poly aluminum-ferric Chloride (PAFC) is a new type of high efficiency coagulant. In this study, high iron type gangue is used as a main raw material. It is calcined at 675 °C for 1 h and 3% CaF2 is added to the calcined powder and reacted with 20% hydrochloric acid at 93 °C for 4 h. The leaching ratio of aluminum ions is 90% and that of iron ions is 91%. After Fe2+ ions are oxidized in the filtrate, CaCO3 is used to adjust the pH of the filtrate to 0.7. The microwave power is adjusted to 80 W and the filtrate is radiated for 5 min. After being aged for 24 h, PAFC product is obtained. The prepared PAFC is used to treat mine water and compared with the results of PAC and PAF, the turbidity removal ratio of PAFC is 99.6%, which is greater than 96.4% of PAC and 93.7% of PAF. PAFC is a mixture with different degrees of polymerization. It demonstrates that extracting aluminum and iron ions from high iron content gangue to prepare PAFC by microwave is efficient and feasible.

Untargeted lipidomics reveals specific lipid abnormalities in systemic lupus erythematosus.

OBJECTIVES: To identify potential lipid biomarkers by studying changes in the blood lipid profile of patients with systemic lupus erythematosus (SLE) using lipidomics. METHODS: Serum samples were collected from 115 SLE patients and 115 age- and sex-matched healthy controls (HCs). Lipid profiles were assessed using ultrahigh-performance liquid chromatography coupled with Q Exactive spectrometry, and possible lipid biomarkers were screened and evaluated by univariate and multivariate analyses. RESULTS: Metabolic phenotypes related to SLE disease activity index (SLEDAI) scores were detected in the serum of SLE patients, and these phenotypes indicated the activity of the disease. Alterations in energy metabolism, fatty acid metabolism and other pathways were observed in patients with SLE. Phosphatidylethanolamine (16:0/18:2), lysophosphatidylethanolamine (18:0), and acylcarnitine (11:0) can be used as biomarkers for the clinical diagnosis of SLE, and receiver operating characteristic (ROC) analysis indicated their effectiveness in diagnosing this disease. CONCLUSIONS: Our study identified serum biomarkers related to disease activity in patients with SLE, providing a basis for its clinical diagnosis.

Untargeted serum metabolomics and potential biomarkers for Sjögren's syndrome.

OBJECTIVES: At present, the pathogenesis of Sjögren's syndrome (SS) remains unclear. This research aimed to identify differential metabolites that contribute to SS diagnosis and discover the disturbed metabolic pathways. METHODS: Recent advances in mass spectrometry have allowed the identification of hundreds of unique metabolic signatures and the exploration of altered metabolite profiles in disease. In this study, 505 candidates including healthy controls (HCs) and SS patients were recruited and the serum samples were collected. A non-targeted gas chromatography-mass spectrometry (GC-MS) serum metabolomics method was used to explore the changes in serum metabolites. RESULTS: We found SS patients and HCs can be distinguished by 21 significant metabolites. The levels of alanine, tryptophan, glycolic acid, pelargonic acid, cis-1-2-dihydro-1-2-naphthalenediol, diglycerol, capric acid, turanose, behenic acid, dehydroabietic acid, stearic acid, linoleic acid, heptadecanoic acid, valine, and lactic acid were increased in serum samples from SS patients, whereas levels of catechol, anabasine, 3-6-anhydro-D-galactose, beta-gentiobiose, 2-ketoisocaproic acid and ethanolamine were decreased. The significantly changed pathways included the following: Linoleic acid metabolism; unsaturated fatty acid biosynthesis; aminoacyl-tRNA biosynthesis; valine, leucine, and isoleucine biosynthesis; glycerolipid metabolism; selenocompound metabolism; galactose metabolism; alanine, aspartate and glutamate metabolism; glyoxylate and dicarboxylate metabolism; glycerophospholipid metabolism; and valine, leucine and isoleucine degradation. CONCLUSIONS: These findings enhance the informative capacity of biochemical analyses through the identification of serum biomarkers and the analysis of metabolic pathways and contribute to an improved understanding of the pathogenesis of SS.

Untargeted lipidomics reveals specific lipid abnormalities in Sjögren's syndrome.

OBJECTIVE: The relationship between serum lipid variations in SS and healthy controls was investigated to identify potential predictive lipid biomarkers. METHODS: Serum samples from 230 SS patients and 240 healthy controls were collected. The samples were analysed by ultrahigh-performance liquid chromatography coupled with Q Exactive™ spectrometry. Potential lipid biomarkers were screened through orthogonal projection to latent structures discriminant analysis and further evaluated by receiver operating characteristic analysis. RESULTS: A panel of three metabolites [phosphatidylcholine (18:0/22:5), triglyceride (16:0/18:0/18:1) and acylcarnitine (12:0)] was identified as a specific biomarker of SS. The receiver operating characteristic analysis showed that the panel had a sensitivity of 84.3% with a specificity of 74.8% in discriminating patients with SS from healthy controls. CONCLUSION: Our approach successfully identified serum biomarkers associated with SS patients. The potential lipid biomarkers indicated that SS metabolic disturbance might be associated with oxidized lipids, fatty acid oxidation and energy metabolism.

Fibrinogen-Like Protein 1 Is a Novel Biomarker for Predicting Disease Activity and Prognosis of Rheumatoid Arthritis.

Rheumatoid arthritis (RA), afflicting over 1% of the population, is an inflammatory joint disease leading to cartilage damage and ultimately impaired joint function. Disease-modifying anti-rheumatic drugs are considered as the first-line treatment to inhibit the progression of RA, and the treatment depends on the disease status assessment. The disease activity score 28 as clinical gold standard is extensively used for RA assessment, but it has the limitations of delayed assessment and the need for specialized expertise. It is necessary to discover biomarkers that can precisely monitor disease activity, and provide optimized treatment for RA patients. A total of 1,244 participants from two independent centers were divided into five cohorts. Cohorts 1-4 constituted sera samples of moderate to high active RA, low active RA, RA in remission and healthy subjects. Cohort 5 consisted of sera of RA, osteoarthritis (OA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (pSS) and healthy subjects. Biomarkers were found from cohorts 1-2 (screening sets), cohort 3 (discovery and external validation sets), cohort 4 (drug intervention set) and cohort 5 (biomarker-specific evaluation set). We found 68 upregulated and 74 downregulated proteins by TMT-labeled proteomics in cohort 1, and fibrinogen-like protein 1 (FGL1) had the highest area under the receiver operating characteristic curve (AUC) values in cohort 2. In cohort 3, in cross-comparison among moderate/high active RA, low active RA, RA in remission and healthy subjects, FGL1 had AUC values of approximately 0.9000 and predictive values of 90%. Additionally, FGL1 had a predictive value of 91.46% for moderate/high active RA vs. remission/low active RA and 80.77% for RA in remission vs. low active RA in cohort 4. Importantly, FGL1 levels had no significant difference in OA and AS compared with healthy persons. The concentrations in SLE and pSS were improved, but approximately 3-fold lower than that in active RA in cohort 5. In summary, FGL1 is a novel and specific biomarker that could be clinically useful for predicting progression of RA.

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics.

Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory rheumatic disease that affects 0.2% of the population. Current diagnostic criteria for disease activity rely on subjective Bath Ankylosing Spondylitis Disease Activity Index scores. Here, we aimed to discover a panel of serum protein biomarkers. First, tandem mass tag (TMT)-based quantitative proteomics was applied to identify differential proteins between 15 pooled active AS and 60 pooled healthy subjects. Second, cohort 1 of 328 humans, including 138 active AS and 190 healthy subjects from two independent centers, was used for biomarker discovery and validation. Finally, biomarker panels were applied to differentiate among active AS, stable AS, and healthy subjects from cohort 2, which enrolled 28 patients with stable AS, 26 with active AS, and 28 healthy subjects. From the proteomics study, a total of 762 proteins were identified and 46 proteins were up-regulated and 59 proteins were down-regulated in active AS patients compared to those in healthy persons. Among them, C-reactive protein (CRP), complement factor H-related protein 3 (CFHR3), α-1-acid glycoprotein 2 (ORM2), serum amyloid A1 (SAA1), fibrinogen γ (FG-γ), and fibrinogen ß (FG-ß) were the most significantly up-regulated inflammation-related proteins and S100A8, fatty acid-binding protein 5 (FABP5), and thrombospondin 1 (THBS1) were the most significantly down-regulated inflammation-related proteins. From the cohort 1 study, the best panel for the diagnosis of active AS vs healthy subjects is the combination of CRP and SAA1. The area under the receiver operating characteristic (ROC) curve was nearly 0.900, the sensitivity was 0.970%, and the specificity was 0.805% at a 95% confidence interval from 0.811 to 0.977. Using 0.387 as the cutoff value, the predictive values reached 92.00% in the internal validation set (62 with active AS vs 114 healthy subjects) and 97.50% in the external validation phase (40 with active AS vs 40 healthy subjects). From the cohort 2 study, a panel of CRP and SAA1 can differentiate well among active AS, stable AS, and healthy subjects. For active AS vs stable AS, the area under the ROC curve was 0.951, the sensitivity was 96.43%, the specificity was 88.46% at a 95% confidence interval from 0.891 to 1, and the coincidence rate was 92.30%. For stable AS vs healthy humans, the area under the ROC curve was 0.908, the sensitivity was 89.29%, the specificity was 78.57% at a 95% confidence interval from 0.836 to 0.980, and the coincidence rate was 83.93%. For active AS vs healthy subjects, the predictive value was 94.44%. The results indicated that the CRP and SAA1 combination can potentially diagnose disease status, especially for active or stable AS, which will be conducive to treatment recommendation for patients with AS.