Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation.

Appraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it's quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former's effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p < 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices.

Eisenia fetida-driven vermitechnology for the eco-friendly transformation of steel waste slag into organic amendment: An insight through microbial diversity and multi-model approach.

The processing of steel waste slag from the black metallurgical sector seriously threatened the ecology. To counter these dangers, appropriate detoxification methods were required. Vermitechnology was one such strategy that could successfully convert this industrial waste into nutrient-rich products suitable for use in agriculture. This research primarily focuses on employing vermitechnology for the transformation of waste steel slag into vermicompost and to determine changes in microbial composition, nutrient cycling, and metal detoxification facilitated by earthworms (Eisenia fetida). Earthworm populations in steel waste vermibeds (sw-vermibeds) increased by 2.87-3.07 folds. T1(SW + CD-1:1) comparatively showed increased levels of nutrients such as nitrogen, phosphorus, and potassium. Microbial and enzymatic parameters were more pronounced in treatment T1. The findings of phospholipid fatty acid (PLFA) diversity demonstrate microbial diversity and fatty acid composition. Based on PLFA Sobol Sensitivity Analysis (SSA), PUFA and cyclo were the most sensitive inputs to the presence of heavy metal (HMs) concentrations in SW. In accordance with Taylor-based modelling, R-tree, and Mars were the most trusted regression models for predicting HMs toxicity on microbes. The bioavailable metal fractions of HMs (Fe, Ni, Cd, Cu, Pb, and Cr) decreased by 61-83%. The correlation was performed for 0 and 90 days for metal microbial interactions r (0 days), [BSR vs Fe, Cd, Cu, Ni = -0.99, -0.82, -0.43, -0.99] and r (90 days), [FDA vs Fe, Cu, Ni = -0.97, -0.47, -0.95]. Overall, the results indicated that T1(1:1 SW + CD) provided more favorable conditions for the development of microbes and Eisenia fetida. This research presents a new perspective to the world community on the transformation of harmful steel waste slag into advantageous biological resources by introducing a novel method of employing Eisenia fetida to remediate hazardous steel waste slag.

Vermitechnology transforms hazardous red mud into benign organic input for agriculture: Insights on earthworm-microbe interaction, metal removal, and soil-crop improvement.

Bioremediation of hazardous bauxite residues, red mud (RM), through vermicomposting has yet to be attempted. Therefore, the valorization potential of Eisenia fetida in various RM and cow dung (CD) mixtures was compared to aerobic composting. Earthworm fecundity and biomass growth were hindered in RM + CD (1:1) feedstock but enhanced in RM + CD (1:3). The pH of highly alkaline RM-feedstocks sharply reduced (>17%) due to vermicomposting. N, P, and K availability increased dramatically with Ca and Na reduction under vermicomposting. Additionally, ∼40-60% bioavailable metal fractions were transformed to obstinate (organic matter and residual bound) forms upon vermicomposting. Consequently, the total metal concentrations were significantly reduced with considerably high earthworm bioaccumulation. Microbial growth and enzyme activity were more significant under vermicomposting than composting. Correlation statistics revealed that microbial augmentation significantly facilitated a metal reduction in RM-vermibeds. Eventually, RM-vermicompost stimulated sesame growth and improved soil health with the least heavy metal contamination to soil and crop.

Elucidating the synergistic effect of acidity and metalloid poisoning on the microbiome through metagenomics and machine learning approaches.

The abundance and diversity of the microflora in a complex environment such as soil is everchanging. Mica mining has led to metalloid poisoning and changes in soil biogeochemistry affecting the overall produce and leading to toxic dietary exposure. The study focuses on two prominent stressors acidity and arsenic, in mining-contaminated agricultural locations. Soil samples were collected from agricultural fields at a distance of 50 m (zone 1) and 500 m (zone 2) from active mines. Mean arsenic concentration was higher in zone 1 and pH was lower. Geostatistical and self-organizing maps were employed to report that the pattern of localization of soil acidity and arsenic content is similar indicating a causal relationship. Cluster and principal component analysis were further used to materialize a negative effect of soil acidity fractions and arsenic labile pool on soil enzymatic activity (fluorescein diacetate, dehydrogenase, ß-1,4-glucosidase, phosphatase, and urease), respiration and Microbial biomass carbon. Soil metagenomic analysis revealed significant differences in the abundance of microbial populations with zone 1 (contaminated zone) having lower alpha and beta diversity. Finally, the efficacy of several machine-learning tools was tested using Taylor diagrams and an effort was made to select a potent algorithm to predict the causal stressors responsible for depreciating soil microbial health. Random Forrest had superior predictive power based on numerical evidence and was therefore chosen as the best-fitted model. The aforementioned insights into soil microbial health and sustenance in stressed conditions can be beneficial for predicting remedial strategies and practicing sustainable agriculture.

From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination.

Fluoride (F-) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies. A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 ± 0.02 ppm) and Unsafe (3.69 ± 0.3 ppm) groups differed significantly (t-value = -10.04, p < 0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3-, HCO3-, SO42-, Cl-, Ca2+, Mg2+, K+, Na+ and F-) were recorded from samples of each group. The samples from 'Unsafe group' showed alkaline pH, the abundance of Na+ and HCO3- ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F- abundance. Aspatial distribution map of F- contamination was created, pinpointing the "contaminated pockets." Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F- contamination in groundwater and the developed models can help predicting fluoride contamination in other areas. The combination of different methodologies (Fuzzy-TOPSIS, machine learning algorithms, and classical methods) results in a synergistic effect where the strengths of each approach compensate for the limitations of the other.

Exorbitant signatures of pesticides and pharmaceuticals in municipal solid wastes (MSWs): Novel insights through risk analysis, dissolution dynamics, and model-based source identification.

Studies on the occurrence and fates of emerging organic micropollutants (EOMPs) like pharmaceuticals and pesticides in MSWs are scarce in the literature. Therefore, MSWs were sampled from 20 Indian landfills and characterized for five widely consumed EOMPs (chlorpyrifos, cypermethrin, carbofuran, carbamazepine, and sodium diclofenac), physicochemical, and biological properties. The pesticide (median: 0.17-0.44 mg kg-1) and pharmaceutical (median: 0.20-0.26 mg kg-1) concentrations significantly fluctuated based on landfill localities. Eventually, principal component and multi-factor (MFA) models demonstrated close interactions of EOMPs with biological (microbial biomass and humification rates) and chemical (N, P, K, Ca, S, etc.) properties of MSWs. At the same time, the MFA resolved that EOMPs' fates in MSWs significantly differ from bigger cosmopolitan cities to smaller rural townships. Correspondingly, the concentration-driven ecological risks were high in 15 MSWs with EOMP-toxicity ranks of diclofenac > carbofuran = chlorpyrifos > cypermethrin > carbamazepine. The EOMPs' dissolution dynamics and source apportionments were evaluated using the positive matrix factorization (PMF) model for the first time on experimental data, extracting four anthropogenic sources (households, heterogeneous business centers, agricultural, and open drains). The most significant contribution of EOMPs to MSWs was due to heterogeneous business activity. Notably, the aging of soluble chemical fractions seems to influence the source characteristics of EOMPs strongly.

Appraise potassium chemistry and distribution patterns in tailing soil, India: Through quantity - Intensity relations and multi model statistical methods.

Tailings are waste materials left behind after mineral extraction. Giridih district of Jharkhand, India has the second largest ore of mica mines in the country. This study evaluated the forms of potassium (K+) and quantity-intensity relationships in soils contaminated by tailings around the abundant mica mines. A total of 63 rice rhizosphere soil samples (8-10 cm depth) were collected from agricultural fields near 21 mica mines in the Giridih district at different distances: 10 m (zone 1), 50 m (zone 2), and 100 m (zone 3). The samples were collected to quantify various forms of potassium in the soil and characterize non-exchangeable K (NEK) reserves and Q/I isotherms. The semi-logarithmic release of NEK with continuous extractions suggests a decrease in release over time. Significant values of threshold K+ levels were observed in zone 1 samples. As K+ concentrations increased, the activity ratio (AReK) and its corresponding labile K+ (KL) concentrations decreased. The AReK, KL, and fixed K+ (KX) values were higher in zone 1 [AReK: 3.2 (mol L-1)1/2 × 10-4, KL: 0.058 cmol kg-1, and KX: 0.038 cmol kg-1), except for readily available K+ (K0) for zone 2 (0.028 cmol kg-1). The potential buffering capacity and K+ potential values were higher in zone 2 soils. In zone 1, Vanselow selectivity coefficients (KV) and Krishnamoorthy-Davis-Overstreet selectivity coefficients (KKDO) were higher, while Gapon constants were higher in zone 3. It was found that AReK was significantly correlated with K0, KL, K+ saturation, -ΔG, KV, and KKDO. Different statistical methods such as positive matrix factorization, self-organizing maps, geostatistics, and Monte Carlo simulation approaches were employed to predict soil K+ enrichment, source apportionment, distribution patterns, availability for plants, and contribution to soil K+ maintenance. Thus, this study significantly contributes to understanding K+ dynamics in mica mine soils and operational K+ management.

Assessing pollution and health risks from chromite mine tailings contaminated soils in India by employing synergistic statistical approaches.

Potentially toxic elements (PTEs) contamination in the agricultural soil can generate a detrimental effect on the ecosystem and poses a threat to human health. The present work evaluates the PTEs concentration, source identification, probabilistic assessment of health hazards, and dietary risk analysis due to PTEs pollution in the region of the chromite-asbestos mine, India. To evaluate the health risks associated with PTEs in soil, soil tailings and rice grains were collected and studied. The results revealed that the PTEs concentration (mainly Cr and Ni) of total, DTPA-bioavailable, and rice grain was significantly above the permissible limit in site 1 (tailings) and site 2 (contaminated) as compared with site 3 (uncontaminated). The Free ion activity model (FIAM) was applied to detect the solubility of PTEs in polluted soil and their probable transfer from soil to rice grain. The hazard quotient values were significantly higher than the safe (FIAM-HQ < 0.5) for Cr (1.50E+00), Ni (1.32E+00), and, Pb (5.55E+00) except for Cd (1.43E-03), Cu (5.82E-02). Severity adjustment margin of exposure (SAMOE) results denote that the PTEs contaminated raw rice grain has high health risk [CrSAMOE: 0.001; NiSAMOE: 0.002; CdSAMOE: 0.007; PbSAMOE: 0.008] for humans except for Cu. The Positive matrix factorization (PMF) along with correlation used to apportion the source. Self-organizing map (SOM) and PMF analysis identified the source of pollution mainly from mines in this region. Monte Carlo simulation (MCS) revealed that TCR (total carcinogenic risk) cannot be insignificant and children were the maximum sufferers relative to adults via ingestion-pathway. In the spatial distribution map, the region nearer to mine is highly prone to ecological risk with respect to PTEs pollution. Based on appropriate and reasonable evaluation methods, this work will help environmental scientists and policymakers' control PTEs pollution in agricultural soils near the vicinity of mines.

Pollution and health risk assessment of mine tailings contaminated soils in India from toxic elements with statistical approaches.

The rapid mining activities of mica mines in Giridih district, India, have led to toxic metal pollution of agricultural soil. This is a key concern for environmental risk and human health. 63 top soil samples were collected at a distance of 10 m (Zone 1), 50 m (Zone 2), and 100 m (Zone 3) from near 21 mica mines with agriculture fields. The mean concentration of total and bio-available toxic elements (TEs - Cr, Ni, Pb, Cu, Zn, and Cd) was higher in zone 1 across three zones. The Positive matrix factorization model (PMF) and Pearson Correlation analysis were used to identify waste mica soils with TEs. Based on PMF results, Ni, Cr, Cd, and Pb were the most promising pollutants and carried higher environmental risks than the other TEs. Using the self-organizing map (SOM), zone 1 was identified as a high-potential source of TEs. Soil quality indexes for TEs risk zone 1 were found to be higher across three zones. Based on the health risk index (HI), children are more adversely affected than adults. Monte Carlo simulations (MCS) model and sensitivity analysis of total carcinogenic risk (TCR), children were more affected by Cr and Ni than adults through ingestion exposure pathways. Finally, a geostatistical tool was developed to predict the spatial distribution patterns of TEs contributed by mica mines. In a probabilistic assessment of all populations, non-carcinogenic risks appeared to be negligible. The fact that there is a TCR can't be ignored, and children are more likely to develop it than adults. Mica mines with TEs contamination were found to be the most significant anthropogenic contributor to health risks based on source-oriented risk assessment.

Assessing the arsenic-saturated biochar recycling potential of vermitechnology: Insights on nutrient recovery, metal benignity, and microbial activity.

Biochar mediated pollutant removal is gaining attention because of high efficiency of the process. However, effective recycling avenues of the pollutant-saturated biochars are scarce in the knowledge base; while such materials can be a new source of long-range contamination. Therefore, potential of vermitechnology for eco-friendly recycling of pollutant-loaded biochar was assessed by using arsenic-saturated native (NBC) and exfoliated (EBC) biochars as feedstocks for the first time. Interestingly, the bioavailable arsenic fractions (water soluble and exchangeable) considerably reduced by 22-44 % with concurrent increment (~8-15 %) of the recalcitrant (residual and organic bound) fractions in the biochar-based feedstocks. Consequently, ~2-3 folds removal of the total arsenic was achieved through vermicomposting. The earthworm population growth (2.5-3 folds) was also highly satisfactory in the biochar-based feedstocks. The results clearly imply that Eisenia fetida could compensate the arsenic-induced stress to microbial population and greatly augmented microbial biomass, respiration and enzyme activity by 3-12 folds. Moreover, biochar-induced alkalinity was significantly neutralized in the vermibeds, which remarkably balanced the TOC level and nutrient (N, P, and K) availability particularly in EBC + CD vermibeds. Overall, the nutrient recovery potential and arsenic removal efficiency of vermitechnology was clearly exhibited in NBC/EBC + CD (12.5:87.5) feedstocks. Hence, it is abundantly clear that vermitechnology can be a suitable option for eco-friendly recycling of pollutant-saturated sorbing agents, like biochars.

Condensation Product of p-anisaldehyde and L-phenylalanine: Fluorescent "on-off" Sensor for Cu2+ and IMPLICATION Logic Gate.

(Z)-2-(4-methoxybenzylideneamino)-3-phenylpropanoic acid (L) synthesized by condensation of p-anisaldehyde and L-phenylalanine acts as selective fluorescent as well as voltammetric sensor for Cu2+ in 2:1 (v/v) CH3OH:H2O. The fluorescence intensity of L (λmax 425 nm) is quenched ca. 65% by Cu2+. Metal ions - Li+, Na+, K+, Al3+, Cu2+, Zn2+, Cd2+, Hg2+, Mn2+, Ni2+ and Pb2+ do not interfere. The binding constant and the detection limits were calculated to be 0.56 × 102 M-1 and 10-6 M respectively. DFT and TDDFT calculations confirmed 2:1 binding stoichiometry between L and Cu2+ obtained from fluorescence data. The interaction between L and Cu2+ is reversible for many cycles with respect to ethylenediamine tetraacetate anion (EDTA2-) which results in IMPLICATION logic gate.

Detoxification and eco-friendly recycling of brick kiln coal ash using Eisenia fetida: A clean approach through vermitechnology.

Brick kiln coal ashes (BKCAs) are one of the major toxic byproducts of the rapidly growing construction industry in developing countries. However, eco-friendly recycling avenues for BKCAs are yet to be explored. The major objectives of the present research were to evaluate the viability of vermitechnology in transforming BKCAs into valuable products, and to examine the metal detoxification potential of Eisenia fetida BKCA-based feedstocks. BKCAs were mixed in large scale with cow dung (CD) in 1:1 and 2:1 ratios, for vermicomposting and aerobic composting; performance was assessed in comparison with CD. Vermiconverted-BKCA was then used as organic fertilizer for rice grown in poorly fertile soil. Acidic nature of BKCA feedstocks was neutralized by 30-86% in the vermireactors. Total N and available P concentrations significantly increased in the vermireactors supplemented with considerable mineralization of total organic C. Exorbitantly high K and S contents were pacified to a normal range after vermicomposting. Greater improvement in microbial biomass, respiration, fungal and bacterial growth was observed under vermicomposting against aerobic composting. Consequently, urease and phosphatase activity increased by 1-4 folds in the BKCA based vermibeds. Bioavailability of toxic metals reduced by 41-74% in the vermicomposted BKCAs. High metal accumulation by the earthworms resulted in substantial reduction of pollution load in the finished product. The field experiment demonstrated that vermiconverted-BKCA could be utilized as potential organic fertilizer for rice production, soil fertility rejuvenation, and metal detoxification. Overall, the study reveals that E. fetida could be used as an efficient contender for sanitization of toxic BKCAs.

Pd-NiO-Y/CNT nanofoam: a zeolite-carbon nanotube conjugate exhibiting high durability in methanol oxidation.

A Pd-NiO-based catalyst hybridized with zeolite-Y and multiwalled carbon nanotubes has been found to show a remarkable mass activity in the electrochemical oxidation of methanol with long term durability up to 80 000 s.

Synthesis and characterization of exfoliated biochar from four agricultural feedstock.

Highly porous biochar (BC) structures have been prepared from inexpensive biomasses like rice straw, bamboo, sugarcane waste, and corn cob via a slow pyrolysis technique in nitrogenous atmosphere. A surface engineering technique has been applied to enhance the surface-to-volume ratio of each biochar sample and finally compared its characteristics through standard surface and elemental characterization techniques, viz. CHN (carbon, hydrogen, and nitrogen), FTIR (Fourier transform infrared spectroscopy), BET (Brunauer-Emmett-Teller), and SEM (scanning electron microscopy). All the biochar samples were observed to be highly carbonized and aromatized. Exfoliated structures were found to contain more elemental carbon (34.14-77.32%) than its native form (30.92-74.46%). Aromatic hydrocarbon, aromatic C=C, aromatics, aliphatic C-O, aliphatic hydrocarbon, and H-bonded OH groups were found to predominate in the surface of biochar structures independent of their precursor composition and extent of exfoliation. SEM micrographic images clearly ensured about the unoriented sheets like the morphology of different biochar samples. Although no significant structural difference was found to exist depending on their precursor compositions, quantitative enhancement of porosity was found to be observed after exfoliation. Both native (240.65 m2/g) and exfoliated (712.89 m2/g) biochars derived from sugarcane wastes were observed to have a maximum surface area in comparison to the biochars derived from rice straw (native, 22.08 m2/g; exfoliated, 29.92 m2/g), bamboo (native, 42.08 m2/g; exfoliated, 248.38 m2/g), and corn cob (native, 136.62 m2/g; exfoliated, 221.71 m2/g). Exfoliated biochars were found to be consistently more potent in comparison to its native form as per our comparative characterizations performed so far.

Formation of Polymeric Housene Molecules of Group 15 Elements (N, P, As, and Sb): A DFT Study.

Recently, the formation of the dimeric stibahousene molecule, bis(stibahousene), has been reported. In line with the report, the formation of dimeric housene molecules with N, P, and As is examined in light of density functional theory. Moreover, the extension of the study from dimeric to tetrameric and hexameric molecules (N, P, As, and Sb) is also performed. The study supports the formation of such polymeric housene analogues.

Impacts of integrated nutrient management on methane emission, global warming potential and carbon storage capacity in rice grown in a northeast India soil.

Rice soil is a source of emission of two major greenhouse gases (methane (CH4) and nitrous oxide (N2O)) and a sink of carbon dioxide (CO2). The effect of inorganic fertilizers in combination with various organics (cow dung, green manure (Sesbania aculeata) Azolla compost, rice husk) on CH4 emission, global warming potential, and soil carbon storage along with crop productivity were studied at university farm under field conditions. The experiment was conducted in a randomized block design for 2 years in a monsoon rice (cv. Ranjit) ecosystem (June-November, 2014 and 2015). Combined application of inorganic (NPK) with Sesbania aculeata resulted in high global warming potential (GWP) of 887.4 kg CO2 ha-1 and low GWP of 540.6 kg CO2 ha-1 was recorded from inorganic fertilizer applied field. Irrespective of the type of organic amendments, flag leaf photosynthesis of the rice crop increased over NPK application (control). There was an increase in CH4 emission from the organic amended fields compared to NPK alone. The combined application of NPK and Azolla compost was effective in the buildup of soil carbon (16.93 g kg-1) and capacity of soil carbon storage (28.1 Mg C ha-1) with high carbon efficiency ratio (16.9). Azolla compost application along with NPK recorded 15.66% higher CH4 emission with 27.43% yield increment over control. Azolla compost application significantly enhanced carbon storage of soil and improved the yielding ability of grain (6.55 Mg ha-1) over other treatments.

Plant extract-mediated green silver nanoparticles: Efficacy as soil conditioner and plant growth promoter.

Recently, concerns have been raised regarding the ultimate fate of silver nanoparticles (SNPs) after their release into the environment. In this study, the environmental feasibility of plant leaf (Thuja occidentalis) extract-mediated green SNPs (GSNPs) was assessed in terms of their effects on soil physicochemical properties and crop growth in comparison to conventionally synthesized silver nanoparticles (CSNPs). Upon application of GSNPs, soil pH shifted toward neutrality, and substantial increments were observed in water holding capacity (WHC), cation exchange capacity (CEC), and N/P availability. The mechanism behind the enhanced availability of N was verified through lab-scale experiments in which GSNP-treated soils efficiently resisted nitrate leaching, thereby sustaining N availability in root zone soil layers. However, retardation in nutrient availability and enzyme activity was apparent in soils treated with 100 mg kg-1 of either CSNPs or GSNPs. Remarkable improvements in leaf area index (LAI), leaf number, chlorophyll content, nitrate reductase (NR) activity, and Phaseolus vulgaris pod yield were observed after the application of low doses of GSNPs (25-50 mg kg-1). The true benefit of GSNP application to soil was substantiated through experiments on plant uptake of nutrients, NR expression, and ferredoxin gene expression in P. vulgaris leaves.

Density Functional Study on the Adsorption of 5-Membered N-Heterocycles on B/N/BN-Doped Graphene: Coronene as a Model System.

Adsorption of seven 5-membered N-heterocycles on B/N/BN-doped graphene (with coronene as a model system) has been studied using density functional theory (DFT). The geometry of the complexes validated the involvement of both π···π stacking and N-H···π interaction in the adsorption process. The stability of the complexes is measured in terms of stabilization energy, and the results suggested that the complexes are stable enough (stabilization energies are in the range of 7.61-14.77 kcal mol-1). Studies confirmed the stability of complexes in the solvent phase too irrespective of the dielectric of the solvent. Dispersive force is the major mode of interaction in stabilizing the complexes. Natural bond orbital analysis indicated a small contribution from electrostatic and covalent interactions. Thermochemical analysis revealed that the complexation is exothermic in nature and favorable at a lower temperature. Adsorption of N-heterocycles exerts a nominal impact on the electronic properties of the undoped/doped graphene. The study presents a simple approach to introduce an arbitrary functionality to undoped/doped graphene by preserving its electronic properties.

Environmental footprints of brick kiln bottom ashes: Geostatistical approach for assessment of metal toxicity.

Coal fired brick kiln factories generate significant of brick kiln bottom ash (BKBA) that contaminate soil and water environments of areas near the dumping sites through leaching of toxic metals (Pb, Cr, Cd, Zn, Mn, and Cu). However, characteristics and environmental effects of BKBAs are yet unknown. We collected BKBA samples from 32 strategic locations of two rapidly developing States (West Bengal and Assam) of India. Scanning electron microscope images indicated spherical and granular structures of BKBAs produced in West Bengal (WBKBA) and Assam (ABKBA) respectively; while energy dispersive spectroscopy and analytical assessments confirmed substantial occurrence of total organic C and nutrient elements (N, P, K, Ca, Mg, and S) in both the BKBAs. FTIR analysis revealed greater predominance of organic matter in ABKBAs than WBKBAs. Occurrence of toxic metals (Cd, Cr, Pb, Zn, Mn, and Cu) was higher in ABKBAs than in WBKBAs; while organic and residual fractions of metals were highly predominant in most of the BKBAs. Principal component analysis showed that metal contents and pH were the major distinguishing characteristics of the BKBAs generated in the two different environmental locations. Human health risk associated with BKBAs generated in Assam is of significant concern. Finally, geo-statistical tools enabled to predict the spatial distribution patterns of toxic metals contributed by the BKBAs in Assam and West Bengal respectively. Assessment of contamination index, geo-accumulation index, and ecological risk index revealed some BKBAs to be more toxic than others.

Exploring Cation-π Interaction in the Complexes with B≡B Triple Bond: A DFT Study.

Density functional theory calculations on metal ion-π interactions in cation-π complexes of diboryne and sandwiches of diboryne and benzene formed via metal ions were performed to understand the strength of interaction in these complexes. Results suggest that apart from the smaller metal ions (Li+, Be2+), larger ions (Na+, Mg2+, Ca2+, and Al3+) can also form cation-π complexes with B≡B triple bond and interaction energies of the complexes with larger metal ions (possessing same charges) are less than those obtained with smaller ions. Cations with higher charge lead to stronger interaction with the B≡B triple bonds. The calculated interaction energy further reveals that the sandwiches are more stable than their corresponding cation-π complexes. Stability of the complexes is measured in terms of global hardness (using Koopmans' theorem and ΔSCF method), and the values obtained using ΔSCF method corroborate with the stability trend predicted by interaction energy values. Negative solvent-phase interaction energy for most of the chosen complexes indicates their stability in polar solvents too.