Simultaneous Cr(VI) reduction and methylene blue removal by \u3ci\u3eBacillus\u3c/i\u3e sp. JH2-2 isolated from mining site soil

A Bacillus sp. strain (JH2-2), isolated from soil at an abandoned mine site, reduced Cr(VI) to Cr(III) under aerobic conditions. At pH 7, the strain reduced essentially all of the Cr(VI) in M9 minimal medium to Cr(III) at initial concentrations ≤50 mg Cr(VI) L−1 within 100 h. The X-ray diffraction pattern of the Cr(III) precipitate matched chromium (III) hydrogen phosphate (CrH2P3O10∙2H2O). The JH2-2 strain showed high tolerance to other heavy metal (loid)s, with minimal inhibitory concentrations in liquid medium of (mg L−1): As (500), Cd (100), Cu (350), Ni (300), Zn (200), and Pb (1800). JH2-2 also promoted decolorization of methylene blue dye. Decolorization was faster in the presence of 10 mg Cr(VI) L−1 than in the absence of Cr(VI). A lag in decolorization at 30 and 50 mg Cr(VI) L−1 is likely due to initial toxicity and inhibition of bacterial growth. The chemistry of MB is complicated by its reduction to colorless leucomethylene blue, which can reoxidize to MB. However, aeration of the solution did not restore measurable MB, supporting removal of the dye via biosorption. Results indicate the bioremediation potential of Bacillus sp. JH2-2 for simultaneous Cr(VI) reduction and methylene blue removal from contaminated water

Sustainable removal of pernicious arsenic and cadmium by a novel composite of MnO2 impregnated alginate beads: A cost-effective approach for wastewater treatment

There is a dire necessity of developing low cost waste water treatment systems, for the efficient removal of noxious heavy metals (and metalloids) such as Arsenic (As) and Cadmium (Cd). Magnetic biopolymer (CABs-MO) was synthesized by the entrapment of nanocrystalline MnO2 in the polymeric microcapsules of calcium alginate (CABs). Batch experiments were conducted under constant pH (6.5), temperature (25OC), different initial concentrations (30–300 mg L−1) and contact times (0–48 h) to study the adsorption isotherms and removal kinetics of pristine (CABs) and hybrid biopolymer (CABs-MO) for the removal of As and Cd. The pseudo-equilibrium process was mathematically well explained by the pseudo-second-order kinetic (R2 ≥ 0.99) and Langmuir isotherm model (R2 ≥ 0.99) with the highest monolayer sorption capacity of 63.6 mg g−1 for Cd on CABs-MO. The As removal rate was maximum up to 6.5 mg g−1 after 12 h of contact period in a single contaminant system than in the mixed contaminant (As + Cd) system (0.8 mg g−1), though the effect was non-significant for Cd (p < 0.05; t-test). The performance of the 10 mM HCl as a regenerating agent was superior (for As in comparison to Cd, p < 0.05; t-test) compared to distilled water (DW) through three to five regeneration cycles. Therefore, the obtained results clearly validate the feasibility of CABs-MO as a potential promising adsorbent for removing metal contaminants from the wastewater. Further research is required to study the decontamination of emerging contaminants with such novel composite beads characterized by varied physico-chemical properties.by Jaehong Shima, Manish Kumar, Santanu Mukherjee and Ritusmita Goswami

Effect of Biochar Amendments on the Co-Composting of Food Waste and Livestock Manure

The global increase in population will result in increased global food production which can, in turn, lead to excessive food waste. Although composting is widely adopted for the conversion of organic waste into value-added products, there are several limitations, such as its lower efficiency in composting food waste without co-composting, the loss of nutrients, and the emission of greenhouse gases. Due to its renowned characteristics, biochar amendments are used during composting to overcome these issues; each waste should be at an appropriate level to yield good quality compost with high nutrient levels. In this study, we co-composted food waste with chicken and swine manure with varying proportions in the presence and absence of biochar to identify the ideal proportion of each raw material and the biochar. Physicochemical parameters such as pH, EC, temperature, bulk density, porosity, C:N ratio, and gaseous emissions were analyzed. The results showed that the desired quality of compost was obtained in the treatment with 5% biochar with 40%, 20%, and 20% of food waste, chicken manure, and swine manure, respectively, and 15% sawdust

Composting Process and Gas Emissions during Food Waste Composting under the Effect of Different Additives

This study investigated the effects of adding mature compost (MC) and vermicompost (VC) on controlling gas emissions and compost quality during food waste (FW) composting. In addition to a control treatment (only food waste), four treatments were designed to mix the initial FW with varying rates of MC and VC (5.0% and 7.5%). The composting process was monitored for 84 days. Results indicate that the addition of MC and VC resulted in higher temperature, prolonged the thermophilic stage and reduced NH3 and greenhouse gas (GHG) emissions. Compared to the control, the loss of NH3-N was decreased by 29&ndash;69%, and the global warming impact was also mitigated by 49&ndash;61%. The largest reductions in NH3 and global warming potential (GWP) were found for 7.5% VC and 5% MC, respectively. The treatments with additives more rapidly achieved the required maturity value. This research suggests that the addition of 7.5% MC and VC is suitable for food waste composting

Prediction and Interpretation of Polymer Properties Using the Graph Convolutional Network

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (Tg), melting temperature (Tm), density (??), and elastic modulus (E) with substantial dependence on the dataset, which is the best for Tg (R2 ??? 0.9) and worst for E (R2 ??? 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with Tg, as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property

Removal of p-cresol and tylosin from water using a novel composite of alginate, recycled MnO2 and activated carbon

For sustainable production, there is an urgent need to minimize the adverse environmental impacts of swine farming, which is a major contributor of the pollutants p-cresol and tylosin. Novel reactive composite alginate beads (CAB-MOACs) were fabricated by combining alginate with activated carbon (AC) and MnO2 recovered from spent battery waste and used for efficient removal of p-cresol and tylosin from water. Batch experiments were carried out under varying pH (3–11), temperature (15–50 °C), and agitation speed (50–200 rpm) to understand their effects on removal efficiency. The CAB-MOACs had better removal efficiency for p-cresol and tylosin than alginate beads alone or beads containing only AC or MnO2. Adsorption to CAB-MOACs followed pseudo-second-order kinetics (R2≥0.98) and Langmuir isotherm models (R2≥0.95). CAB-MOACs showed higher removal efficiency (∼99.9% after 10 h) compared to beads containing only immobilized MnO2 (60–70%) or AC (94–96%). Regeneration and reuse performance of the CAB-MOACs was excellent through five cycles, although slightly better for p-cresol than tylosin. With low-cost manufacturing and beneficial utilization of hazardous waste such as spent batteries, the newly developed composite beads show potential as an effective adsorbent for treating wastewater effluent containing emerging contaminants like p-cresol and tylosin. Future studies may focus on product refinement and large-scale testing on actual wastewaters.by Jaehong Shim, Manish Kumar, Ritusmita Goswami,Payal Mazumder, Byung-Taek Oh and Patrick J. She

Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction

Polyamides are often used for their superior thermal, mechanical, and chemical properties. They form a diverse set of materials that have a large variation in properties between linear to aromatic compounds, which renders the traditional quantitative structure–property relationship (QSPR) challenging. We use extended connectivity fingerprints (ECFP) and traditional QSPR fingerprints to develop machine learning models to perform high fidelity prediction of glass transition temperature (Tg), melting temperature (Tm), density (ρ), and tensile modulus (E). The non-linear model using random forest is in general found to be more accurate than linear regression; however, using feature selection or regularization, the accuracy of linear models is shown to be improved significantly to become comparable to the more complex nonlinear algorithm. We find that none of the models or fingerprints were able to accurately predict the tensile modulus E, which we hypothesize is due to heterogeneity in data and data sources, as well as inherent challenges in measuring it. Finally, QSPR models revealed that the fraction of rotatable bonds, and the rotational degree of freedom affects polyamide properties most profoundly and can be used for back of the envelope calculations for a quick estimate of the polymer attributes (glass transition temperature, melting temperature, and density). These QSPR models, although having slightly lower prediction accuracy, show the most promise for the polymer chemist seeking to develop an intuition of ways to modify the chemistry to enhance specific attributes

Efficacy and field applicability of Burmese grape leaf extract (BGLE) for cadmium removal: an implication of metal removal from natural water

Burmese Grape Leaf Extract (BGLE), a low cost adsorbent was studied for cadmium (Cd(II)) removal from metal solutions and natural water samples. Batch adsorption studies were carried out to examine the influence of contact time and initial metal concentration after characterization under scanning electron microscopy (SEM). Cd(II)adsorptiononto BGLE was best explained by pseudo-second order kinetics (R2 = 0.99) and best fitted with Langmuir isotherm model (R2 = 0.76). Beside the selective adsorption activity of BGLE towards Cd(II), only 0.1g of BGLE have shown effective adsorption of these ions with a maximum adsorption capacity (qm) of 44.72mgg-1. This study was a unique combination of laboratory experiments and field implication. Study indicates that same efficacy cannot be obtained in natural water samples as obtained in the case of laboratory due to the interference of major ions in water.by Rinkumoni Borah, Deepa Kumari, Anindita Gogoi, Sunayana Biswas, Ritusmita Goswami, Jaehong Shim, Naznin Ara Begum and Manish Kuma

Novel 4,7-Dithien-2-yl-2,1,3-benzothiadiazole-based Conjugated Copolymers with Cyano Group in Vinylene Unit for Photovoltaic Applications

Two novel conjugated copolymers utilizing 4,7-dithien-2-yl-2,1,3-benzothiadiazole (DTBT) coupled with cyano (-CN) substituted vinylene, as the electron deficient moeity, have been synthesized and evaluated in bulk heterojunction solar cell. The electron deficient moeity was coupled with carbazole and fluorene unit by Knoevenagel condition to provide poly(bis-2,7-((Z)-1-cyano-2-(5-(7-(2-thienyl)-2,1,3-benzothiadiazol-4-yl)-2-thienyl-ethenyl)-alt-9-(1-octylnonyl)-9H-carbazol-2-yl-2-butenenitrile) (PCVCNDTBT) and poly(bis-2,7((Z)-1-cyano-2-(5-(7-(2-thienyl)-2,1,3-benzothiadiazol-4-yl)-2-thienyl)ethenyl)-alt-9,9-dihexyl-9H-fluoren-2-yl) (PFVCNDTBT). The optical band gaps of PCVCNDTBT (1.74 eV) and PFVCNDTBT (1.80 eV) are lower than those of PCDTBT (1.88 eV) and PFVDTBT (2.13 eV), which is advantageous to provide better coverage of the solar spectrum in the longer wavelength region. The high V-oc value of the PSC of PCVCNDTBT (similar to 0.91 V) is attributed to its lower HOMO energy level (-5.6 eV) as compared to PCDTBT (-5.5 eV). Bulk heterojunction solar cells based on the blends of the polymers with [6,6]phenyl-C-61-butyric acid methyl ester (PC61BM) gave power conversion efficiencies of 0.76% for PCVCNDTBT under AM 1.5, 100 mW/cm(2).close