A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Artificial intelligence as a prediction tool for orthognathic surgery assessment.

INTRODUCTION: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. METHODS: A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). RESULTS: The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). CONCLUSIONS: The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.

Study on the Relationship between the Structure and Pyrolysis Characteristics of Lignin Isolated from Eucalyptus, Pine, and Rice Straw through the Use of Deep Eutectic Solvent.

Understanding the pyrolysis product distributions of deep eutectic solvent (DES)-isolated lignins (DESLs) from different types of biomass is of great significance for lignin valorization. The structure and pyrolysis properties of DESLs obtained from eucalyptus (E-DESL), pine (P-DESL), and rice straw (R-DESL) were studied through the use of various methods such as elemental analysis, GPC, HS-GC, and NMR techniques, and the pyrolysis characteristics and product distributions of the DESLs were also further investigated through the use of TGA, Py-GC/MS, and tubular furnace pyrolysis. DESLs with high purity (88.5-92.7%) can be efficiently separated from biomass while cellulose is retained. E-DESL has a relatively low molecular weight, and P-DESL has a relatively higher hydrogen-carbon effective ratio and a lower number of condensation structures. The Py-GC/MS results show that, during DESL pyrolysis, the monomeric aromatic hydrocarbons, p-hydroxyphenyl-type phenols, and catechol-type phenols are gradually released when the guaiacyl-type phenols and syringyl-type phenols decrease with the rising temperature. 4-methylguaiacol and 4-methylcatechol, derived from the guaiacyl-type structural units, are positively correlated with temperature, which causes a significant increase in products with a side-chain carbon number of 1 from P-DESL pyrolysis. 4-vinylphenol, as a representative product of the R-DESL, derived from p-hydroxyphenyl-type structural units, also gradually increased. In addition, the P-DESL produces more bio-oil during pyrolysis, while gases have the highest distribution in E-DESL pyrolysis. It is of great significance to study the characteristic product distribution of lignin isolated through the use of DES for lignin directional conversion into specific high-value aromatic compounds.

Efficient Regulation of Polysulfides by Anatase/Bronze TiO2 Heterostructure/Polypyrrole Composites for High-Performance Lithium-Sulfur Batteries.

Despite having ultra-high theoretical specific capacity and theoretical energy density, lithium-sulfur (Li-S) batteries suffer from their low Coulombic efficiency and poor lifespan, and the commercial application of Li-S batteries is seriously hampered by the severe "shuttle effect" of lithium polysulfides (LiPSs) and the large volume expansion ratio of the sulfur electrode during cycling. Designing functional hosts for sulfur cathodes is one of the most effective ways to immobilize the LiPSs and improve the electrochemical performance of a Li-S battery. In this work, a polypyrrole (PPy)-coated anatase/bronze TiO2 (TAB) heterostructure was successfully prepared and used as a sulfur host. Results showed that the porous TAB could physically adsorb and chemically interact with LiPSs during charging and discharging processes, inhibiting the LiPSs' shuttle effect, and the TAB's heterostructure and PPy conductive layer are conducive to the rapid transport of Li+ and improve the conductivity of the electrode. By benefitting from these merits, Li-S batteries with TAB@S/PPy electrodes could deliver a high initial capacity of 1250.4 mAh g-1 at 0.1 C and show an excellent cycling stability (the average capacity decay rate was 0.042% per cycle after 1000 cycles at 1 C). This work brings a new idea for the design of functional sulfur cathodes for high-performance Li-S battery.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications.

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

A highly selective electrochemical sensor based on molecularly imprinted polypyrrole-modified gold electrode for the determination of glyphosate in cucumber and tap water.

An electrochemical sensor based on molecularly imprinted polypyrrole (MIPPy) was developed for selective and sensitive detection of the herbicide glyphosate (Gly) in cucumber and tap water samples. The sensor was prepared via synthesis of molecularly imprinted polymers on a gold electrode in the presence of Gly as the template molecule and pyrrole as the functional monomer by cyclic voltammetry (CV). The sensor preparation conditions including the ratio of template to functional monomers, number of CV cycles in the electropolymerization process, the method of template removal, incubation time, and pH were optimized. Under the optimal experimental conditions, the DPV peak currents of hexacyanoferrate/hexacyanoferrite changed linearly with Gly concentration in the range from 5 to 800 ng mL-1, with a detection limit of 0.27 ng mL-1 (S/N = 3). The sensor was used to detect the concentration of Gly in cucumber and tap water samples, with recoveries ranging from 72.70 to 98.96%. The proposed sensor showed excellent selectivity, good stability and reversibility, and could detect the Gly in real samples rapidly and sensitively. Graphical abstract Schematic illustration of the experimental procedure to detect Gly using the MIPPy electrode.

Application of Y-shaped, coated self-expandable metallic stents for anastomotic stenosis after gastrojejunostomy (Billroth II).

BACKGROUND: Anastomotic stenosis is an infrequent but life-threatening complication after gastrojejunostomy (Billroth II). Tubular or single tubular stents have limited efficacy due to the particular anatomy. PURPOSE: To assess the feasibility of a Y-shaped, fully-coated, self-expandable, metallic stent (SEMS) for anastomotic stenosis after gastrojejunostomy (Billroth II). MATERIAL AND METHODS: Between January 2008 and August 2014, 14 patients (10 with gastric carcinoma and four with duodenal ulcers) had anastomotic stenoses following Billroth II reconstructions. Eight patients with gastric cancer had tumor recurrence near the anastomosis; two had benign strictures. The four duodenal ulcer patients had benign stenoses. An integrated Y-shaped, fully coated SEMS was designed to accord with the anatomy of residual gastrojejunal anastomotic strictures. Fourteen stents were inserted under fluoroscopic control. Follow-up was at 1, 3, 9, and 12 months, and then annually. RESULTS: All 14 stents were inserted successfully at the first attempt with a technical success rate of 100%. After stenting, abdominal symptoms resolved in all patients. All patients were followed up for 4-27 months (mean, 13.9 months). One of the eight recurrent cases died of multiple tumor metastases and liver failure after 7 months, without obstruction symptoms. In all six patients with benign anastomotic stenosis, the stents were removed successfully without complication and with no evidence of restenosis based on clinical evaluation and imaging. CONCLUSION: A Y-shaped, fully-coated SEMS proved to be a feasible and minimally invasive procedure for treating anastomotic stenosis after gastrojejunostomy (Billroth II).

Deep Eutectic Solvents (DESs) for the Isolation of Willow Lignin (Salix matsudana cv. Zhuliu).

Deep eutectic solvents (DESs) are a potentially high-value lignin extraction methodology. DESs prepared from choline chloride (ChCl) and three hydrogen-bond donors (HBD)-lactic acid (Lac), glycerol, and urea-were evaluated for isolation of willow (Salix matsudana cv. Zhuliu) lignin. DESs types, mole ratio of ChCl to HBD, extraction temperature, and time on the fractionated DES-lignin yield demonstrated that the optimal DES-lignin yield (91.8 wt % based on the initial lignin in willow) with high purity of 94.5% can be reached at a ChCl-to-Lac molar ratio of 1:10, extraction temperature of 120 °C, and time of 12 h. Fourier transform infrared spectroscopy (FT-IR) , 13C-NMR, and 31P-NMR showed that willow lignin extracted by ChCl-Lac was mainly composed of syringyl and guaiacyl units. Serendipitously, a majority of the glucan in willow was preserved after ChCl-Lac treatment.

Differential developmental and proinflammatory responses of zebrafish embryo to repetitive exposure of biodigested polyamide and polystyrene microplastics.

Microplastics (MPs) have attracted global concern and are at the forefront of current research on environmental pollution, whereas, little is known about the degradation of ingested MPs in the gastrointestinal environment and repetitive exposure-associated risk of egested MPs to organisms. The present study revealed that polyamide (PA) and polystyrene (PS) MPs exhibited remarkably differential biodegradations in the gastric and intestinal fluids of a model fish (Siniperca chuatsi). Significant disintegration of the skeleton structure, size reduction (from 27.62 to 9.17 µm), benzene ring scission, and subsequent biogenic corona coating and surface oxidation occurred during in vitro digestion, thus increasing the hydrophilicity and agglomeration of PS. Conversely, PA MPs exhibited high resistance to enzymolysis with slight surface erosions and protein adsorption. Relative to the pristine form, the bioaccumulation of digested PS elevated and the musculoskeletal deformity and mortality of juvenile zebrafish were obviously enhanced, but these changes were unobservable for PA. Lipopolysaccharide-triggered inflammation and apoptosis via Toll-like receptor signaling pathways and reduction of extracellular matrix secretions driven by oxidative stress contributed to the aggravated inhibitory effects of digested PS on larval development. These findings emphasize the necessity of concerning the biota digestion in MP risk assessments in natural waters.

Stretchable and transparent alginate ionic gel film for multifunctional sensors and devices.

Flexible and stretchable substrates based on pure natural polymers have attracted widespread attention for next-generation "green" electronics. However, fabrication of stretchable and "green" electronic sensors with integrated high stretchability, optical transmittance and good conductivity still remains tremendous challenges. Herein, alginate ionic gel films (AIGFs) with integrated high stretchability (tensile strength of 4.13 MPa and 191.1 % fracture strain) and excellent transparent properties (transparency of ∼92 %) are achieved by the glycerol inducing physical crosslinking and CaCl2 initiating ionic crosslinking, a simple soaking and drying strategy. The obtained gel films not only exhibit good ionic conductivity, but also high reliability, wide-range sensing, and multiple sensitivity to external stimulus. More importantly, these ionic conductive gel films as green substrates are successfully utilized for construction of flexible and patterned optoelectronic devices. This promising strategy will open up new powerful routes to construct highly stretchable, transparent, and ionic conductive substrates for multifunctional sensors and devices.

Stretchable and sensitive sodium alginate ionic hydrogel fibers for flexible strain sensors.

Ionic conductive hydrogel fibers based on natural polymers provide an immense focus for a new generation of electronics due to their flexibility and knittability. The feasibility of utilizing pure natural polymer-based hydrogel fibers could be drastically improved if their mechanical and transparent performances satisfy the requirements of actual practice. Herein, we report a facile fabrication strategy for significantly stretchable and sensitive sodium alginate ionic hydrogel fibers (SAIFs), by glycerol initiating physical crosslinking and by CaCl2 inducing ionic crosslinking. The obtained ionic hydrogel fibers not only show significant stretchability (tensile strength of 1.55 MPa and fracture strain of ∼161 %), but also exhibit wide-range sensing, satisfactorily stable, rapidly responsive, and multiply sensitive abilities to external stimulus. In addition, the ionic hydrogel fibers have excellent transparency (over 90 % in a wide wavelength range), and good anti-evaporation and anti-freezing properties. Furthermore, the SAIFs have been easily knitted into a textile, and successfully applied as wearable sensors to recognize human motions, by observing the output electrical signals. Our methodology for fabrication intelligent SAIFs will shed light on artificial flexible electronics and other textile-based strain sensors.

Effect of deep eutectic solvents-regulated lignin structure on subsequent pyrolysis products selectivity.

The chemical structure of lignin has an important effect on the lignin pyrolysis product distributions. Therefore, it is of great significance to regulate the selectivity of pyrolysis products by modifying the lignin structure. Herein, deep eutectic solvents (DESs) including choline chloride/ethylene glycol (CE), zinc chloride/ethylene glycol (ZE) and choline chloride/acetic acid, treatment of softwood kraft lignin (SKL) is demonstrated. Systematic characterization indicate that the DESs are not only highly conducive to increasing the hydrogen to carbon efficient ratio, reducing the molecular weight and ß-O-4 linkage, but also contributes to the maximum degradation rate and thermal stability of SKL. Noticeably, CE and ZE treatment are significantly improved the amount of H-phenols and C-phenols derived lignin pyrolysis, respectively. In addition, DESs pretreatment are also beneficial to the increment of monomer aromatic hydrocarbons. More importantly, the CE pretreatment contributes to the improvement of bio-oil yield and decrease of char content from lignin pyrolysis.

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models.

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-ß1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-ß1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

Novel Copper Nanoparticles Intercalated Polyurethane Heparin/Poly-L-Lysine Chelates Coated Stents: Viability Study for Coronary Vascular Cells and Aneurysms Treatments.

Continuous delayed endothelium regeneration and continues thrombosis development designate a task for coronary artery stent rehabilitation. To progress the direct vascular cell behavior, aneurysms treatments and compatibility of cardiovascular implants novel copper intercalated polyurethane heparin/poly-L-lysine chelates treated stent has established in this report. The functional group modifications, structural characteristics, and stability of the chelates have investigated for polyurethane heparin: poly-L-lysine, copper intercalated polyurethane heparin/poly-L-lysine coated stents. The FTIR results showed the copper intercalation at 446 cmr and the Cu 2s peak at 932 eV from XPS also indicated that the successful coating of copper, polyurethane heparin, poly-L-lysine. The relative surface geomorphology of the chelates displayed the uniform Cu coating consisting of multilayer poly-L-lysine on the substrate. The stability and biocompatibility studies indicated the significantly enhanced performance with clot the APTT and TT periods as clotting and cell proliferation assessments. This type of composite proposes a stage on a stent external area for discerning track of vascular cell performance and aneurysms treatments with low side effects.

TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis.

Osteoarthritis is a chronic disease that affects the temporomandibular joint (TMJ), causing chronic pain and disability. To diagnose patients suffering from this disease before advanced degradation of the bone, we developed a diagnostic tool called TMJOAI. This machine learning based algorithm is capable of classifying the health status TMJ in of patients using 52 clinical, biological and jaw condyle radiomic markers. The TMJOAI includes three parts. the feature preparation, selection and model evaluation. Feature generation includes the choice of radiomic features (condylar trabecular bone or mandibular fossa), the histogram matching of the images prior to the extraction of the radiomic markers, the generation of feature pairwise interaction, etc.; the feature selection are based on the p-values or AUCs of single features using the training data; the model evaluation compares multiple machine learning algorithms (e.g. regression-based, tree-based and boosting algorithms) from 10 times 5-fold cross validation. The best performance was achieved with averaging the predictions of XGBoost and LightGBM models; and the inclusion of 32 additional markers from the mandibular fossa of the joint improved the AUC prediction performance from 0.83 to 0.88. After cross-validation and testing, the tools presented here have been deployed on an open-source, web-based system, making it accessible to clinicians. TMJOAI allows users to add data and automatically train and update the machine learning models, and therefore improve their performance.

Effect of the interaction of phenolic hydroxyl with the benzene rings on lignin pyrolysis.

In this study, lignin with different phenolic hydroxyl contents and five model compounds are pyrolyzed to investigate the effect of the interaction of phenolic hydroxyl with a benzene ring on lignin pyrolysis. The results demonstrated that phenolic hydroxyl can reduce the stability of lignin and promote the elimination of the side chain on lignin during pyrolysis. The repolymerization during lignin pyrolysis, which results in increased activation energy and char yield during pyrolysis, can be mainly attributed to phenolic hydroxyl. Meanwhile, the repolymerization because of phenolic hydroxyl is obviously affected by the electron cloud density of the benzene ring. The repolymerization caused by the phenolic hydroxyl can be effectively reduced by increasing the electron cloud density. Furthermore, regulation of the product distribution obtained via lignin pyrolysis by changing the electron cloud density of the benzene ring and the phenolic hydroxyl content in lignin is proposed.

Effect of pre-acetylation of hydroxyl functional groups by choline chloride/acetic anhydride on subsequent lignin pyrolysis.

In this work, an emerging and efficient strategy for the preparation of lignin samples with different acetylation degree by choline chloride/acetic anhydride (ChCl/Aa) treatment of pine kraft lignin (PKL) is reported, and the effects of efficient pre-acetylation of hydroxyl functional groups on subsequent lignin pyrolysis are also systematically investigated. The results show that the ChCl/Aa displays a high acetylation efficiency towards the aliphatic hydroxyl (~99.1%) and phenolic hydroxyl (~94.0%) of PKL, which enhances the hydrogen to carbon effective ratio of PKL. Noticeably, the ChCl/Aa has a slight effect on ß-O-4 of PKL, and the acetylation of hydroxyl is beneficial for the improvement of the maximum degradation rate of PKL. In addition, the acetylation of hydroxyl is also significantly contributed to the increment of the pyrolysis bio-oil yield. Importantly, the relative content of the H-phenols obtained from acetylated lignins pyrolysis shows a positive correlation with the acetylation degree of hydroxyl.

Structural characterization and comparison of enzymatic and deep eutectic solvents isolated lignin from various green processes: Toward lignin valorization.

In this work, several representative green processes were developed to extract the enzymatic lignin and deep eutectic solvents (DESs) isolated lignin from corn straw. The results revealed that enzymatic lignin and DESs isolated lignin had a relatively low and homogeneous molecular weight and DESs isolated lignin shown a higher purity. Enzymatic and DESs isolated lignin showed good representativeness and similar to original herbal lignin structures accompany few aryl ether linkage cleavages and oxidation phenomenon. Among them, the subcritical CO2-assisted autohydrolysis and ChCl/Lac DESs treatment exhibited a higher severity for lignin preparation, and sequence DESs isolated lignin had a better reactivity. The ß-O-4 ether bonds and carbon-carbon bonds linkage were further broken up during the Lac and DESs sequence treatment. In short, the described processes showed practical significance for lignin extraction and potential valorization, as well as help to develop more novel strategies for the current biorefinery process.

Cotransport and deposition of biochar with different sized-plastic particles in saturated porous media.

Plastic particles recently are reported to be ubiquitous in natural environments, while biochar is widely employed as an amendment into natural environments e.g. soil for pollution remediation. It is highly likely their transport behaviors in porous media would be altered due to interaction with each other. In this study, the cotransport and deposition behaviors of biochar and plastic particles (0.02 µm nanoplastics (NPs), 0.2 µm and 2 µm micrometer-plastics (MPs)) in quartz sand were investigated at both low (5 mM) and high ionic strength (25 mM) in NaCl solutions at pH 6. The results demonstrated that smaller sized plastic particles (0.02 µm NPs and 0.2 µm MPs) increased biochar transport under both 5 and 25 mM NaCl solutions, while large plastic particles (2 µm MPs) increased biochar transport in 5 mM NaCl solutions yet decreased biochar transport in 25 mM NaCl solutions. For all sized plastic particles, biochar significantly decreased their transport at both low (5 mM) and high (25 mM) ionic strengths in NaCl solutions. The change of zeta potentials of biochar due to heteroaggregation with plastic particles, and the steric repulsion induced by the suspended plastic particles contributed to the enhanced biochar transport with the copresence of smaller sized plastic particles (0.02 µm NPs and 0.2 µm MPs). While, the cotransport of biochar with 2 µm MPs due to their heteroaggregation contributed to the varied transport behaviors of biochar observed at low and high ionic strength with the copresence of 2 µm MPs. The formation of plastic particles-biochar heteroaggregates drove to the decreased transport of all three different sized plastic particles with the copresence of biochar. Considering the coexistence of biochar and plastic particles in natural environment, they would interact with each other (form heteroaggregates) and thus lead to the change of transport behaviors in porous media.

Treatment of anthraquinone dye textile wastewater using anaerobic dynamic membrane bioreactor: Performance and microbial dynamics.

The performance and microbial community structure of anaerobic dynamic membrane bioreactor (AnDMBR) treating textile wastewater was investigated. The reactor showed excellent soluble COD and color removal of 98.5% and >97.5%, respectively. Dynamic membrane layer grown over the 3D printed dynamic membrane support showed decent rejection for high molecular weight compounds (>20 kDa); and the total suspended solid rejection by the dynamic layer was >98.8%. Gel permeation chromatography analysis of extracellular polymeric substance (EPS) and effluent samples revealed EPS accounted for more than 76.7% of low molecular weight fractions (<20 kDa) that end up in the effluent. Higher applied flux facilitated the rapid formation dynamic layer which enabled a satisfactory effluent quality. Microbial community analysis revealed that during the operation the archaeal community was relatively stable while obvious changes took place in the bacterial community. Introduction of dye Remazol Brilliant Blue R (RBBR) to the AnDMBR increased the abundances of phyla of Proteobacteria and Spirochaetae whereas fractions of Firmicutes and Euryarchaeota decreased obviously. Furthermore, relative stable abundances of phyla Aminicenantes, Bacteroidetes, Thermotogae and Chloroflexi among the top six phyla detected in the system ensured a healthy anaerobic degradation environment for RBBR wastewater treatment.