CmBr confers fruit bitterness under CPPU treatment in melon.

Many biotic or abiotic factors such as CPPU (N-(2-chloro-pyridin-4-yl)-N'-phenylurea), a growth regulator of numerous crops, can induce bitterness in cucurbits. In melon, cucurbitacin B is the major compound leading to bitterness. However, the molecular mechanism underlying CuB biosynthesis in response to different conditions remains unclear. Here, we identified a set of genes involved in CPPU-induced CuB biosynthesis in melon fruit and proposed CmBr gene as the major regulator. Using CRISPR/Cas9 gene editing, we confirmed CmBr's role in regulating CuB biosynthesis under CPPU treatment. We further discovered a CPPU-induced MYB-related transcription factor, CmRSM1, which specifically binds to the Myb motif within the CmBr promoter and activates its expression. Moreover, we developed an introgression line by introducing the mutated Cmbr gene into an elite variety and eliminated CPPU-induced bitterness, demonstrating its potential application in breeding. This study offers a valuable tool for breeding high-quality non-bitter melon varieties and provides new insights into the regulation of secondary metabolites under environmental stresses.

Optimization of fermentation parameters to improve the biosynthesis of selenium nanoparticles by Bacillus licheniformis F1 and its comprehensive application.

BACKGROUND: Selenium nanoparticles (SeNPs) are increasingly gaining attention due to its characteristics of low toxicity, high activity, and stability. Additionally, Bacillus licheniformis, as a probiotic, has achieved remarkable research outcomes in diverse fields such as medicine, feed processing, and pesticides, attracting widespread attention. Consequently, evaluating the activity of probiotics and SeNPs is paramount. The utilization of probiotics to synthesize SeNPs, achieving large-scale industrialization, is a current hotspot in the field of SeNPs synthesis and is currently the most promising synthetic method. To minimize production costs and maximize yield of SeNPs, this study selected agricultural by-products that are nutrient-rich, cost-effective, and readily available as culture medium components. This approach not only fulfills industrial production requirements but also mitigates the impact on downstream processes. RESULTS: The experimental findings revealed that SeNPs synthesized by B. licheniformis F1 exhibited a spherical morphology with diameters ranging from 110 to 170 nm and demonstrating high stability. Both the secondary metabolites of B. licheniformis F1 and the synthesized SeNPs possessed significant free radical scavenging ability. To provide a more robust foundation for acquiring large quantities of SeNPs via fermentation with B. licheniformis F1, key factors were identified through single-factor experiments and response surface methodology (RSM) include a 2% seed liquid inoculum, a temperature of 37 ℃, and agitation at 180 rpm. Additionally, critical factors during the optimization process were corn powder (11.18 g/L), soybean meal (10.34 g/L), and NaCl (10.68 g/L). Upon validating the optimized conditions and culture medium, B. licheniformis F1 can synthesize nearly 100.00% SeNPs from 5 mmol/L sodium selenite. Subsequently, pilot-scale verification in a 5 L fermentor using the optimized medium resulted in a shortened fermentation time, significantly reducing production costs. CONCLUSION: In this study, the efficient production of SeNPs by the probiotic B. licheniformis F1 was successfully achieved, leading to a significant reduction in fermentation costs. The exploration of the practical applications of this strain holds significant potential and provides valuable guidance for facilitating the industrial-scale implementation of microbial synthesis of SeNPs.

Enhancing uric acid electrochemical detection with copper ion-activated mini protein mimicking uricase within ZIF-8: response surface methodology (RSM) optimization.

This study aims to investigate the influence of copper(II) ions as a cofactor on the electrochemical performance of a biocomposite consisting of a mini protein mimicking uricase (mp20) and zeolitic immidazolate framework-8 (ZIF-8) for the detection of uric acid. A central composite design (CCD) was utilized to optimize the independent investigation, including pH, deposition potential, and deposition time, while the current response resulting from the electrocatalytic oxidation of uric acid was used as the response. The statistical analysis of variance (ANOVA) showed a good correlation between the experimental and predicted data, with a residual standard error percentage (RSE%) of less than 2% for predicting optimal conditions. The synergistic effect of the nanoporous ZIF-8 host, Cu(II)-activated mp20, and reduced graphene oxide (rGO) layer resulted in a highly sensitive biosensor with a limit of detection (LOD) of 0.21 µM and a reproducibility of the response (RSD = 0.63%). The Cu(II)-activated mp20@ZIF-8/rGO/SPCE was highly selective in the presence of common interferents, and the fabricated layer exhibited remarkable stability with signal changes below 4.15% after 60 days. The biosensor's reliable performance was confirmed through real sample analyses of human serum and urine, with comparable recovery values to conventional HPLC.

Application of Response Surface Methodology (RSM) in the statistical evaluation of biodiesel production from the neutral lipids of the Coelastrella-Nannochloropsis consortium.

The paramount challenge in economically workable microalgal biodiesel production is the selection of a competent catalyst to improve the fatty acid methyl ester yield with desirable fatty acid composition. Though countless researchers have explored different homogeneous and heterogeneous catalysts to improve the transesterification efficacy, achieving greater biodiesel production from the neutral lipids of the microalgal consortium using a statistical tool, response surface methodology is scarce. Thus, the present study applied Response surface methodology to statistically analyze the biodiesel production from the neutral lipids of the indigenous Coelastrella-Nannochloropsis consortium (CNC) on the way to commercial feasibility. Onset of the study, the neutral lipids and acid value of the CNC were determined to be 18.74% and 2.73%, respectively. The transesterification of the neutral lipids of CNC was optimized through the coded factors in the RSM for various reaction parameters as combined influence viz., (i) Catalyst dose: methanol volume, (ii) Catalyst dose: reaction time; (iii) Catalyst dose: reaction temperature, (iv) Time: temperature, (v) time: methanol volume, (vi) temperature: methanol volume. Based on the ANOVA, coefficient determination, 2% KOH, 2 h time, 70 °C temperature, and 9 mL methanol volume were ascertained to be optimal values to accomplish 92% biodiesel production. Further, the biodiesel has desirable palmitic, palmitoleic, stearic, oleic, linoleic, and linolenic acids, with palmitic acid as the prevalent fatty acid contributing 16-18%. In addition, the tested fuel properties of CNC biodiesel satisfy international biodiesel standards.

Optimizing backwash control using data on seasonal changes in the invertebrate community of granular activated carbon filters.

Problems associated with the colonization and leakage of invertebrates in the granular activated carbon (GAC) filters of waterworks have received increased attention in recent years. To study the effect of environmental factors and water quality on invertebrate abundances, and the backwash control for minimizing invertebrate abundance. A survey of the invertebrate community of GAC filters was carried out monthly from March 2021 to May 2022. A pilot-scale GAC system established in the laboratory alongside a lake, with a volume of 35.3 L. 45 invertebrate species were detected, and 40 of these were rotifers. Significant variation in abundance was observed among seasons before and after GAC filtration, the average invertebrate abundance in the inlet water was 11.1 times that in the filtrate. The GAC filter contained invertebrates that might be responsible for the large number of organisms in the filtrate. Invertebrate abundance in the GAC filter decreased gradually with the carbon layer depth, which the mean invertebrate abundances were 6,926, 5,232, and 3818 ind./kg in the top layer (TL), middle layer (ML), and bottom layer (BL), respectively. Invertebrate abundance was correlated with water temperature and varied seasonally. Among eight water quality parameters, chlorophyll a (Chla) and the total plate count (TPC) were most significantly correlated with invertebrate abundance. According to the statistical modeling and the optimization process of response surface methodology (RSM). The predicted optimal values were a flow rate of 6.36 L/h, a backwash cycle of 3.26 d, and a backwash intensity of 14.97 L/(m2·s) for a minimum invertebrate abundance of 3013 ind./kg in the GAC filter. To maintain invertebrate abundance within an acceptable range, some of these measures might need to be modified depending on the actual conditions.

Eco-friendly engineering of micro composite-based hydroxyapatite bio crystal and polyaniline for high removal of OG dye from wastewater: Adsorption mechanism and RSM@BBD optimization.

The presence of harmful substances such as dyes in water systems poses a direct threat to the quality of people's lives and other organisms living in the ecosystem. Orange G (OG) is considered a hazardous dye. The existing paper attempts to evaluate a low-cost adsorbent for the effective removal of OG dye. The developed adsorbent Polyaniline@Hydroxyapatite extracted from Cilus Gilberti fish Scale (PANI@FHAP) was elaborated through the application of the in situ chemical polymerization method to incorporate PANI on the surface of naturally extracted hydroxyapatite FHAP. The good synthesis of PANI@FHAP was evaluated through multiple techniques including X-ray diffraction (XRD), Scanning electron microscopy coupled with energy dispersive X-ray spectrometry (SEM/EDS), Fourier Transforms Infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) coupled with thermal differential analysis (DTA) analysis. The results reveal a highly ordered disposition of PANI chains on FHAP, resulting in a well-coated FHAP in the PANI matrix. Furthermore, the presence of functional groups on the surface of PANI such as amine (-NH2) and imine (=NH) groups would facilitate the removal of OG dye from contaminated water. The adsorption of OG onto PANI@FHAP was conducted in batch mode and optimized through response surface methodology coupled with box-Behnken design (RSM/BBD) to investigate the effect of time, adsorbent dose, and initial concentration. The outcomes proved that OG adsorption follows a quadratic model (R2 = 0.989). The kinetic study revealed that the adsorption of OG fits the pseudo-second-order model. On the other hand, the isotherm study declared that the Freundlich model is best suited to the description of OG adsorption. For thermodynamic study, the adsorption of OG is spontaneous in nature and exothermic. Furthermore, the regeneration-reusability study indicates that PANI@FHAP could be regenerated and reused up to five successive cycles. Based on the FTIR spectrum of PANI@FHAP after OG adsorption, the mechanism governing OG adsorption is predominantly driven by π-π interaction, electrostatic interaction, and hydrogen bonding interactions. The obtained results suppose that PANI@FHAP adsorbent can be a competitive material in large-scale applications.

Chitosan anchored nZVI bionanocomposites for treatment of textile wastewater: Optimization, mechanism, and phytotoxic assessment.

In recent years, there has been a growing focus on treating textile wastewater due to its escalating threat to aquatic ecosystems and exposed communities. The present study investigates the adsorption efficacy of biopolymer functionalized nanoscale zero-valent iron (CS@nZVI) composite for the treatment of textile wastewater using the RSM-CCD model. The structure and morphology of CS@nZVI were characterized using XRD, FTIR, FESEM, and EDX. CS@nZVI was then evaluated for its adsorption potential in removing COD, color, and other physico-chemical parameters from textile wastewater. The results showed the high efficacy of CS@nZVI for COD and color removal from textile wastewater. Under optimal conditions (pH 6, contact time 60 min, and 1.84 g CS@nZVI), COD removal reached a maximum of 85.53%, and decolorization efficiency was found to be 89.73%. The coefficient of determination R2 (0.98) and AIC (269.75) values suggested quadratic model as the best-fitted model for optimizing the process parameters for COD removal. Additionally, the physico-chemical parameters were found to be within permissible limits after treatment with CS@nZVI. The influence of coexisting ions on COD removal followed the order PO43- > SO42- > Cl- >Na+ > Ca2+. The kinetics data fitted well with the pseudo-first-order reaction, indicating physisorption as the primary mechanism. The thermodynamic study revealed the endothermic nature of the removal process. Reusability tests demonstrated that great regeneration capacity of spent CS@nZVIafter five consecutive cycles. Furthermore, toxicological studies showed reduced toxicity in treated samples, leading to improved growth of Vigna radiata L. These findings suggest that CS@nZVI bionanocomposites could serve as an efficient, cost-effective, and eco-friendly remediation agent for the treatment of textile effluents, presenting significant prospects for commercial applications.

Optimization of operating parameters and microbiological mechanism of a low C/N wastewater treatment system dominated by iron-dependent autotrophic denitrification.

Ferrous iron (Fe2+) reduces the amount of external carbon source used for the denitrification of low-C/N wastewater. The effects of key operating parameters on the efficiency of ferrous-dependent autotrophic denitrification (FDAD) and the functioning mechanism of the microbiome can provide a regulatory strategy for improving the denitrification efficiency of low C/N wastewater. In this study, the response surface method (RSM) was used to explore the influence of four important parameters-the molar ratio of Fe2+ to NO3--N (Fe/N), total organic carbon (TOC), the molar ratio of inorganic carbon to NO3--N (IC/N) and sludge volume (SV, %)-on the FDAD efficiency. Functional prediction and molecular ecological networks based on high-throughputs sequencing techniques were used to explore changes in the structure, function, and biomarkers of the sludge microbial community. The results showed that Fe/N and TOC were the main parameters affecting FDAD efficiency. Higher concentrations of TOC and high Fe/N ratios provided more electron donors and improved denitrification efficiency, but weakened the importance of biomarkers (Rhodanobacter, Thermomonas, Comamonas, Thauera, Geothrix and unclassified genus of family Gallionellaceae) in the sludge ecological network. When Fe/N > 4, the denitrification efficiency fluctuated significantly. Functional prediction results indicated that genes that dominated N2O and NO reduction and the genes that dominated Fe2+ transport showed a slight decrease in abundance at high Fe/N levels. In light of these findings, we recommend the following optimization ranges of parameters: Fe/N (3.5-4); TOC/N (0.36-0.42); IC/N (3.5-4); and SV (approximately 35%).

Evaluating embodied energy, carbon impact, and predictive precision through machine learning for pavers manufactured with treated recycled construction and demolition waste aggregate.

This investigation assesses the embodied energy and carbon footprint in the manufacture of pavers using varying proportions of recycled Construction and Demolition Waste (CDW). Additionally, Thin Film Composite Polyamide fiber (TFC PA), extracted from end-of-life Reverse Osmosis (RO) membranes, is introduced as an additive to enhance the concrete's strength. Machine learning techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Response Surface Methodology (RSM), are employed to predict the mechanical properties of pavers. The study focuses on examining the energy required and embodied carbon in various mix proportions, as well as the mechanical properties-specifically compressive strength and split tensile strength of concrete with different CDW and TFC PA proportions. Findings reveal that the optimal percentage of TFC PA is 3 % for all CDW replacement proportions, resulting in low carbon content both in terms of energy and embodiment and in mechanical behavior. The implementation of ANN and SVR is conducted in MATLAB, while a Design Expert is employed to generate the experimental design for RSM. The RSM regression model demonstrates a robust correlation between variables and observed outcomes, with optimal p-values, R2 values, and f-values. The ANN model successfully captures the variability in the data. Additionally, the findings indicate a consistent superiority of the Support Vector Regression (SVR) model over both Artificial Neural Network (ANN) and Response Surface Model (RSM) models when considering diverse performance metrics such as residuals and correlation coefficients.

Response surface methodology, and artificial neural network model for removal of textile dye Reactive Yellow 105 from wastewater using Zeolitic Imidazolate-67 modified by Fe3O4 nanoparticles.

The applicability of Zeolitic Imidazolate-67, Modified by Fe3O4 Nanoparticles, was studied for removing textile dye Reactive yellow 105 from wastewater by adsorption method using response surface methodology (RSM). For the adsorption characterization of the adsorbent used in HE-4G dye adsorption, BET, FTIR, XRD, and SEM analyses were performed. The impacts of variables, including initial HE-4G dye concentration (X1), pH (X2), adsorbent dosage (X3), and sonication time (X4), the highest removal efficiency as 98%, 10 mg/L initial concentration, pH 6, 0.025 g adsorbent dosage, and 6.0 min time respectively. Adsorption equilibrium and kinetic data it, that data were for the Langmuir isotherm, pseudo-second-order kinetics, and maximum adsorption capacity (105.0 mg/g), respectively. Thermodynamic parameters indicated HE-4G dye adsorption is feasible, spontaneous and exothermic. Promising treatment capabilities of the ZIF-67-Fe3O4NPs have been during the comparative adsorption removal of HE-4G dye from DI water against spiked natural water samples and synthetic Na+, K+, Ca2+, and Mg2+ solutions. The observed outcome is the suitability of the artificial neural network model as a tool for mean square error, (MSEANN = 0.53, and R2 = 0.9926) for removing HE-4G dye. Results that ZIF-67-Fe3O4NPs, like being recyclable, and cost-efficient made it a promising absorbent for wastewater.

Unveiling Morphine: A Rapid and Selective Fluorescence Sensor for Forensic and Medical Analysis.

Opioid use, particularly morphine, is linked to CNS-related disorders, comorbidities, and premature death. Morphine, a widely abused opioid, poses a significant global health threat and serves as a key metabolite in various opioids. Here, we present a turn-off fluorescent sensor capable of detecting morphine with exceptional sensitivity and speed in various samples. The fluorescent sensor was developed through the dimerization process of 7-methoxy-1-tetralone and subsequent demethylation to produce the final product. Despite morphine possessing inherent fluorophoric properties and emitting light in an approximately similar wavelength as the sensor's fluorescent blue light, the introduction of the target molecule (morphine) in the presence of the sensor caused a reduction in the sensor's fluorescence intensity, which is attributable to the formation of the sensor-morphine complex. By utilizing this fluorescence quenching sensor, the chemo-selective detection of morphine becomes highly feasible, encompassing a linear range from 0.008 to 40 ppm with an impressive limit of detection of 8 ppb. Consequently, this molecular probe demonstrates a successful application in determining trace amounts of morphine within urine, yielding satisfactory analytical results. The study also explores the effect of several variables on the sensor's response and optimizes the detection of morphine in urine using a response surface methodology with a central composite design.

Simultaneous synthesis of super-paramagnetic hydrochar in a one-pot using subcritical water medium and evaluation of its photocatalytic activity.

The unregulated release of chemical dyes into the environment presents considerable environmental hazards when left untreated. Photocatalytic degradation, acknowledged as an eco-friendly and cost-effective method, has garnered attention for its efficacy in eliminating organic pollutants like dye. Consequently, the development of multifunctional materials with different applications in environmental and catalytic fields emerges as a promising avenue. Recognizing the significance of integrating catalysts and porous materials for enhancing interactions between pollutants and photo-sensitive substances, magnetic hydrochar emerges as a solution offering heightened efficiency, scalability, recyclability, and broad applicability in various environmental processes, notably wastewater treatment, due to its facile separation capability. In this study, Fe3O4-based, super-paramagnetic hydrochar (SMHC) was simultaneously synthesized in a single step using a coconut shell in the subcritical water medium. A thorough analysis was conducted on both raw hydrochar (RHC) and SMHC to unravel the mechanism of interaction between Fe3O4 nanoparticles and the hydrochar matrix. The synthesized hydrochar exhibited super-paramagnetic characteristics, with a saturation magnetization of 23.7 emu/g and a magnetic hysteresis loop. SMHC displayed a BET surface area of 42.6 m2/g and an average pore size of 26.3 nm, indicating a mesoporous structure according to nitrogen adsorption-desorption isotherms. XRD analysis revealed magnetic crystal sizes in the obtained SMHC to be 13.7 nm. The photocatalytic performance of SMHC was evaluated under visible light exposure in the presence of H2O2 for Astrazon yellow (AY) dye degradation, with optimization conducted using response surface methodology (RSM). The most substantial dye removal, reaching 92.83%, was achieved with 0.4% H2O2 at a 20 mg/L dye concentration and an 80-min reaction duration.

Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning.

The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, "Clean Water and Sanitation". However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43-) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43-, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4-73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43- removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes.

Sudden sulfamethoxazole shock leads to nitrite accumulation in microalgae-nitrifying bacteria consortia: Physiological responses and light regulating strategy.

Antibiotic shock may potentially impact the performance of promising microalgae-nitrifying bacteria consortia (MNBC) processes. This study investigated physiological behaviors of MNBC under sulfamethoxazole (SMX) shock (mg/L level) and verified a light regulating strategy for improving process performance. Results showed that SMX shock did not affect ammonium removal but caused nitrite accumulation, resulting from combined effects of excessive reactive oxidative species (ROS) production, inhibited microalgal photosynthetic activity, upregulated expressions of amoA and hao, and downregulated expression of nxrA. Moreover, high ammonium concentration aggravated nitrite accumulation and reduced ammonium removal owing to significantly reduced dissolved oxygen (DO). Increasing light intensity enhanced microalgal photo-oxygenation and promoted expressions of all nitrification-related genes, thus improving ammonium removal and alleviating nitrite accumulation. A central composite design coupled with response surface methodology (CCD-RSM) further demonstrated the negative impacts of SMX shock and high ammonium on MNBC and the effectiveness of the light regulation in maintaining stable process performance. This study provides theoretical basis for physiological responses and regulatory strategy of the MNBC process facing short-term antibiotic shock.

Degradation of levofloxacin using electro coagulation residuals-alginate beads as a novel heterogeneous electro-fenton composite.

The presence of levofloxacin (LEV) in aqueous solutions can pose health risks to humans, have adverse effects on aquatic organisms and ecosystems, and contribute to the development of antibiotic-resistant bacteria. This study aims to investigate the feasibility of using electrocoagulation residuals (ECRs) as a heterogeneous catalyst in the electro-Fenton process for degrading LEV. By combining electrocoagulation residuals with sodium alginate, ECRs-alginate beads were synthesized as a heterogeneous electro-Fenton composite. The response surface method was employed to investigate the optimization and influence of various operating parameters such as the initial concentration of LEV (10-50 mg/L), voltage (15-35 V), pH (3-9), and catalyst dose (1-9 g/L). The successful incorporation of iron and other metals into the ECRs-alginate beads was confirmed by characterization tests such as EDX and FTIR. By conducting a batch reaction under optimal conditions (initial LEV concentration = 20 mg/L, pH = 4.5, voltage = 30V, and catalyst dose = 7 g/L), a remarkable degradation of 99% for LEV was achieved. Additionally, under these optimal conditions, a high removal efficiency of 92.3% for total organic carbon (TOC) could be attained within 120 min and these findings are remarkable compared to previous studies. The results further indicated that the degradation of levofloxacin (LEV) could be accurately quantified by utilizing the first-order kinetic reaction with a 0.03 min-1 rate constant. The synthesized beads offered notable advantages in terms of being eco-friendly, simple to use, highly efficient, and easily recoverable from the liquid medium after use.

A machine learning approach to investigate the impact of land use land cover (LULC) changes on groundwater quality, health risks and ecological risks through GIS and response surface methodology (RSM).

Groundwater resources are enormously affected by land use land cover (LULC) dynamics caused by increasing urbanisation, agricultural and household discharge as a result of global population growth. This study investigates the impact of decadal LULC changes in groundwater quality, human and ecological health from 2009 to 2021 in a diverse landscape, West Bengal, India. Using groundwater quality data from 479 wells in 2009 and 734 well in 2021, a recently proposed Water Pollution Index (WPI) was computed, and its geospatial distribution by a machine learning-based 'Empirical Bayesian Kriging' (EBK) tool manifested a decline in water quality since the number of excellent water category decreased from 30.5% to 28% and polluted water increased from 44% to 45%. ANOVA and Friedman tests revealed statistically significant differences (p < 0.0001) in year-wise water quality parameters as well as group comparisons for both years. Landsat 7 and 8 satellite images were used to classify the LULC types applying machine learning tools for both years, and were coupled with response surface methodology (RSM) for the first time, which revealed that the alteration of groundwater quality were attributed to LULC changes, e.g. WPI showed a positive correlation with built-up areas, village-vegetation cover, agricultural lands, and a negative correlation with surface water, barren lands, and forest cover. Expansion in built-up areas by 0.7%, and village-vegetation orchards by 2.3%, accompanied by a reduction in surface water coverage by 0.6%, and 2.4% in croplands caused a 1.5% drop in excellent water and 1% increase in polluted water category. However, ecological risks through the ecological risk index (ERI) exhibited a lower risk in 2021 attributed to reduced high-risk potential zones. This study highlights the potentiality in linking LULC and water quality changes using some advanced statistical tools like GIS and RSM for better management of water quality and landscape ecology.

Optimizing soil tetrabromobisphenol A remediation through iron-based activation of persulfate: A comparative analysis of homogeneous and heterogeneous systems.

Tetrabromobisphenol A (TBBPA) that widely exists in soil and poses a potential threat to ecological environment urgently needs economically efficient remediation techniques. This study utilized both homogeneous Fe2⁺ solution and heterogeneous iron-based nanomaterials (chemically synthesized nano zero-valence iron (nZVI) and green-synthesized iron nanoparticles (G-Fe NPs)) to activate persulfate (PS) and assess their efficacy in degrading TBBPA in soil. The results demonstrate the superior performance of heterogeneous catalytic systems (WG-Fe NPs/PS (82.07%) and WnZVI/PS (78.32%)) over homogeneous catalytic system (WFe2+/PS (71.69%)), In addition, G-Fe NPs and nZVI effectively controlled the slow release of Fe2+. The optimization analysis using response surface methodology (RSM) reveal the remarkable significance of the experimental model based on the box-behnken design. RSM show that G-Fe NPs/PS exhibited optimal process parameters and predicted the maximum soil TBBPA degradation efficiency reaching 98.77%. The results of density functional theory calculations suggest that C-Br are the primary targets for electrophilic substitution reactions. Based on the f0 value and △G, the degradation pathway of TBBPA is inferred to involve a sequential debromination process, followed by the cleavage of intermediate carbon-carbon bonds and subsequent oxidation reactions. Hence, G-Fe NPs/PS not only facilitate waste resource utilization but also hold significant application potential.

Production of tannase from a newly isolated yeast, Geotrichum cucujoidarum using agro-residues.

With an aim of producing commercially important tannase enzyme using cheap and readily available agro-residues, leaves of Indian Gooseberry (Phyllanthus emblica) and Jamun (Syzygium cumini), peels of Lemon (Citrus limon), and Pomegranate (Punica granatum) were screened. Newly isolated Geotrichum cucujoidarum was utilized for the study. Preliminary studies indicated that tannase titer obtained is not proportional to the tannin content of the agro-residues and solid state fermentation superior compared to submerged fermentation. Jamun mixed with lemon peel in equal proportion supplemented with minerals under solid-state fermentation gave a tannase titer of 15.46 U/g dry solids. Through successful implantation of Plackett-Burman design, yeast extract concentration, inoculum volume, and amount of substrate were found to be the most significant factors. Further optimization of these three factors through Response Surface Methodology resulted in the 1.7-fold increase in tannase titer. Validation experiments using 3.97 g of Jamun leaves + lemon peel powder mixed with a nutrient solution having (w/v) yeast extract - 1.1%, dextrose - 3%, Urea - 1.125%, potassium chloride - 0.1%, magnesium sulfate heptahydrate - 0.1% with the initial pH of 5, inoculated with 2.48 ml of inoculum gave a tannase titer of 26.43 U/g dry solids after 6 days of solid-state fermentation.

Optimizing the extract yield of bioactive compounds in Valeriana officinalis root: a D-optimal design.

It is estimated that 80% of all synthetic drugs are derived from medicinal plants, and nowadays, many synthetic drugs are derived from medicinal plants. Valeriana officinalis can treat many diseases of the nervous system. A crucial aspect of valerian extract is that it inhibits the proliferation of breast cancer cells. To optimize the yield of bioactive compounds in the V. officinalis root extraction, a response surface methodology-based D-optimal design was used. To fulfill this aim, the effects of various factors such as solvent type and concentration, mixing temperature, ultrasound time, and drying method were examined. The optimal conditions for solvent percentages, mixing temperature, ultrasound time, solvent type, and drying methods were determined to be 94.88%, 25 °C, 48.95 min, methanol, and microwave, respectively, with a desirability of 0.921. The predicted valerenic acid, total phenols, total flavonoids, and antioxidant activity in V. officinalis extract were 1.19 (mg/g DW), 8.22 (mg/g DW), 5.27 (mg/g DW), and 92.64%, respectively. In optimal conditions, the extracted amounts of valerenic acid, total phenols, total flavonoids, and antioxidant activity were 2.07 mg/g DW, 7.96 mg/g DW, 5.52 mg/g DW, and 78.68%, respectively, which were consistent with the model predicted amounts (based on 95% prediction interval). This study could be useful as a model for demonstrating the efficacy of microwave drying to maximize the biochemical content of V. officinalis, as well as the antioxidant activity of the root extracts of V. officinalis on industrial scale.

Exploring the Potential of Biochar Derived from Chinese Herbal Medicine Residue for Efficient Removal of Norfloxacin.

One-step carbonization was explored to prepare biochar using the residue of a traditional Chinese herbal medicine, Atropa belladonna L. (ABL), as the raw material. The resulting biochar, known as ABLB4, was evaluated for its potential as a sustainable material for norfloxacin (NOR) adsorption in water. Subsequently, a comprehensive analysis of adsorption isotherms, kinetics, and thermodynamics was conducted through batch adsorption experiments. The maximum calculated NOR adsorption capacity was 252.0 mg/g at 298 K, and the spontaneous and exothermic adsorption of NOR on ABLB4 could be better suited to a pseudo-first-order kinetic model and Langmuir model. The adsorption process observed is influenced by pore diffusion, π-π interaction, electrostatic interaction, and hydrogen bonding between ABLB4 and NOR molecules. Moreover, the utilization of response surface modeling (RSM) facilitated the optimization of the removal efficiency of NOR, yielding a maximum removal rate of 97.4% at a temperature of 304.8 K, an initial concentration of 67.1 mg/L, and a pH of 7.4. Furthermore, the biochar demonstrated favorable economic advantages, with a payback of 852.5 USD/t. More importantly, even after undergoing five cycles, ABLB4 exhibited a consistently high NOR removal rate, indicating its significant potential for application in NOR adsorption.