A general linear free energy relationship for predicting partition coefficients of neutral organic compounds

Novel Approach to Photocatalytic Removal of Linezolid by Advanced Nano-Biochar/Bismuth Oxychloride Hybrid

Herein, we introduce an innovative nanohybrid material for advanced wastewater treatment, composed of Corchorus olitorius-derived biochar and bismuth oxychloride (Biochar/Bi12O17Cl2), demonstrated in a solar photoreactor. This work focuses on the efficient degradation of linezolid (LIN), a persistent pharmaceutical pollutant, utilizing the unique (photo)catalytic capabilities of the nanohybrid. Compared with its individual components, the biochar/Bi12O17Cl2 hybrid exhibits a remarkable degradation efficiency of 82.6% for LIN, alongside significant chemical oxygen demand (COD) and total organic carbon (TOC) mineralization rates of 81.3 and 75.8%, respectively. These results were achieved within 3 h under solar irradiation, using an optimal composite dose of 125 mg/L at pH 4.3 ± 0.45, with an initial COD and LIN concentrations of 1605 and 160.8 mg/L and TOC of 594.3 mg/L. The nanohybrid's stability across five cycles of use demonstrates its potential for repeated applications, with degradation efficiencies of 82.6 and 77.9% in the first and fifth cycles, respectively. This indicates the biochar/Bi12O17Cl2 composite's suitability as a sustainable and cost-effective solution for the remediation of heavily contaminated waters. Further, the degradation pathway proposed the degradation of all of the generated intermediates to a single-ring compound. Contributing to the development of next-generation materials for environmental remediation, this research underscores the critical role of nanotechnology in enhancing water quality and ecosystem sustainability and addressing the global imperative for clean water access and environmental preservation.

Bacteriogenic synthesis of morphologically diverse silver nanoparticles and their assessment for methyl orange dye removal and antimicrobial activity

Nanotechnology and nanoparticles have gained massive attention in the scientific community in recent years due to their valuable properties. Among various AgNPs synthesis methods, microbial approaches offer distinct advantages in terms of cost-effectiveness, biocompatibility, and eco-friendliness. In the present research work, investigators have synthesized three different types of silver nanoparticles (AgNPs), namely AgNPs-K, AgNPs-M, and AgNPs-E, by using Klebsiella pneumoniae (MBC34), Micrococcus luteus (MBC23), and Enterobacter aerogenes (MBX6), respectively. The morphological, chemical, and elemental features of the synthesized AgNPs were analyzed by using UV-Vis spectroscopy (UV-Vis), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscope (FESEM) and energy-dispersive spectroscopy (EDX). UV-Vis absorbance peaks were obtained at 475, 428, and 503 nm for AgNPs-K, AgNPs-M, and AgNPs-E, respectively. The XRD analysis confirmed the crystalline nature of the synthesized AgNPs, having peaks at 26.2°, 32.1°, and 47.2°. At the same time, the FTIR showed bands at 599, 963, 1,693, 2,299, 2,891, and 3,780 cm-1 for all the types of AgNPs indicating the presence of bacterial biomolecules with the developed AgNPs. The size and morphology of the AgNPs varied from 10 nm to several microns and exhibited spherical to porous sheets-like structures. The percentage of Ag varied from 37.8% (wt.%) to 61.6%, i.e., highest in AgNPs-K and lowest in AgNPs-M. Furthermore, the synthesized AgNPs exhibited potential for environmental remediation, with AgNPs-M exhibiting the highest removal efficiency (19.24% at 120 min) for methyl orange dye in simulated wastewater. Further, all three types of AgNPs were evaluated for the removal of methyl orange dye from the simulated wastewater, where the highest dye removal percentage was 19.24% at 120 min by AgNPs-M. Antibacterial potential of the synthesized AgNPs assessment against both Gram-positive (GPB) Bacillus subtilis (MBC23), B. cereus (MBC24), and Gram-negative bacteria Enterococcus faecalis (MBP13) revealed promising results, with AgNPs-M, exhibiting the largest zone of inhibition (12 mm) against GPB B. megaterium. Such investigation exhibits the potential of the bacteria for the synthesis of AgNPs with diverse morphology and potential applications in environmental remediation and antibacterial therapy-based synthesis of AgNPs.

Unlocking the potential of magnetic biochar in wastewater purification: a review on the removal of bisphenol A from aqueous solution

Bisphenol A (BPA) is an essential and extensively utilized chemical compound with significant environmental and public health risks. This review critically assesses the current water purification techniques for BPA removal, emphasizing the efficacy of adsorption technology. Within this context, we probe into the synthesis of magnetic biochar (MBC) using co-precipitation, hydrothermal carbonization, mechanical ball milling, and impregnation pyrolysis as widely applied techniques. Our analysis scrutinizes the strengths and drawbacks of these techniques, with pyrolytic temperature emerging as a critical variable influencing the physicochemical properties and performance of MBC. We explored various modification techniques including oxidation, acid and alkaline modifications, element doping, surface functional modification, nanomaterial loading, and biological alteration, to overcome the drawbacks of pristine MBC, which typically exhibits reduced adsorption performance due to its magnetic medium. These modifications enhance the physicochemical properties of MBC, enabling it to efficiently adsorb contaminants from water. MBC is efficient in the removal of BPA from water. Magnetite and maghemite iron oxides are commonly used in MBC production, with MBC demonstrating effective BPA removal fitting well with Freundlich and Langmuir models. Notably, the pseudo-second-order model accurately describes BPA removal kinetics. Key adsorption mechanisms include pore filling, electrostatic attraction, hydrophobic interactions, hydrogen bonding, π-π interactions, and electron transfer surface interactions. This review provides valuable insights into BPA removal from water using MBC and suggests future research directions for real-world water purification applications.

Assessment of liquid-liquid partition for the assignment of descriptors for the solvation parameter model

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and McGowan's characteristic volume, V, to model transfer properties between condensed phases. The V descriptor is assigned from structure. For compounds liquid at 20 °C the E descriptor can be assigned from the characteristic volume and its refractive index. The E descriptor for compounds solid at 20 °C and the S, A, and B descriptors are experimental properties traditionally assigned from chromatographic, liquid-liquid partition, and solubility measurements. In this report liquid-liquid partition constants in totally organic and aqueous biphasic systems are evaluated as a standalone technique for descriptor assignments. Using six totally organic biphasic systems the S, A, and B descriptors were assigned with an average absolute deviation (AAD) of about 0.04, 0.03, and 0.04, respectively, compared with the best estimate of the true descriptor values for 65 compounds. The E descriptor for compounds solid at 20 °C can only be estimated with an AAD of approximately 0.1. For six aqueous biphasic systems the B descriptor is assigned with a lower AAD of 0.028 and higher AAD of 0.08 and 0.05 for the S and A descriptors, respectively, than for the totally organic biphasic systems for compounds with a reliable value for the E descriptor. The preferred system for descriptor assignments utilizes both totally organic biphasic systems (heptane-1,1,1-trifluoroethanol, isopentyl ether-propylene carbonate, isopentyl ether-ethanolamine, heptane-ethylene glycol, heptane-formamide, and 1,2-dichloroethane-ethylene glycol) and aqueous biphasic systems (octanol-water, cyclohexane-water) with the possible substitution of some systems with alternative systems of similar selectivity. For 55 varied compounds this combination of eight organic and aqueous biphasic systems resulted in an AAD of approximately 0.03, 0.02, and 0.02 for the S, A, and B descriptors compared to the best estimate of the true descriptor value. For 30 compounds solid at 20 °C the AAD for the E descriptor of 0.11 is poorly assigned. The relative average absolute deviation in percent (RAAD) corresponds to 9.7 %, 3.1 %. 4.0 % and 8.3 % for E, S, A, and B, respectively, for the eight biphasic systems. Liquid-liquid partition is compared to reversed-phase liquid and gas chromatography as a standalone technique for descriptor assignments.

Synthesis, characterization, and antibacterial activity studies of two Co(II) complexes with 2-[(E)-(3-acetyl-4-hydroxyphenyl)diazenyl]-4-(2-hydroxyphenyl)thiophene-3-carboxylic acid as a ligand

Two new Cobalt(II) complexes 12 and 13 have been synthesized from 2-[(E)-(3-acetyl-4-hydroxyphenyl)diazenyl]-4-(2-hydroxyphenyl)thiophene-3-carboxylic acid (11) as a novel ligand. These three new compounds were characterized on the basis of their powder X-Ray Diffraction, UV-Vis, IR, NMR, elemental analysis and MS spectral data. DFT/B3LYP mode of calculations were carried out to determine some theorical parameters of the molecular structure of the ligand. The purity of the azoic ligand and the metal complexes were ascertained by TLC and melting points. The analysis of the IR spectra of the polyfunctionalized azo compound 11 and its metal complexes 12 and 13, reveals that the coordination patterns of the ligand are hexadentate and tetradentate respectively. Based on the UV-Vis electronic spectral data and relevant literature reports, the ligand and derived complexes were assigned the E (trans) isomer form. Likewise, octahedral and square-planar geometries were respectively assigned to the cobalt(II) complexes. The broth microdilution method was used for antibacterial assays through the determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The ligand 11 displayed moderate antibacterial activity (MIC = 32-128 μg/mL) against Staphylococcus aureus ATCC25923, Escherichia coli ATCC25922, Pseudomonas aeruginosa and Klebsiella pneumoniae 22. The octahedral cobalt(II) complex 12 showed moderate activity against Pseudomonas aeruginosa (MIC = 128 μg/mL) and Klebsiella pneumoniae 22 (MIC = 64 μg/mL) and none against Staphylococcus aureus ATCC25923 and Escherichia coli ATCC25922, whereas the square-planar complex 13 displayed moderate activity only on Klebsiella pneumoniae 22 (MIC = 64 μg/mL).

A review of recent advancement in covalent organic framework (COFs) synthesis and characterization with a focus on their applications in antibacterial activity

The primary objective of this review is to present a comprehensive examination of the synthesis, characterization, and antibacterial applications of covalent organic frameworks (COFs). COFs represent a distinct category of porous materials characterized by a blend of advantageous features, including customizable pore dimensions, substantial surface area, and adaptable chemical properties. These attributes position COFs as promising contenders for various applications, notably in the realm of antibacterial activity. COFs exhibit considerable potential in the domain of antibacterial applications, owing to their amenability to functionalization with antibacterial agents. The scientific community is actively exploring COFs that have been imbued with metal ions, such as copper or silver, given their observed robust antibacterial properties. These investigations strongly suggest that COFs could be harnessed effectively as potent antibacterial agents across a diverse array of applications. Finally, COFs hold immense promise as a novel class of materials for antibacterial applications, shedding light on the synthesis, characterization, and functionalization of COFs tailored for specific purposes. The potential of COFs as effective antibacterial agents beckons further exploration and underscores their potential to revolutionize antibacterial strategies in various domains.

Microfluidic-based nanoemulsion of Ocimum basilicum extract: Constituents, stability, characterization, and potential biomedical applications for improved antimicrobial and anticancer properties

This paper reports on the findings from a study that aimed to identify and characterize the constituents of Ocimum basilicum extract using gas chromatography-mass spectrometry (GC-MS) analysis, as well as assess the physicochemical properties and stability of nanoemulsions formulated with O. basilicum extract. The GC-MS analysis revealed that the O. basilicum extract contained 22 components, with Caryophyllene and Naringenin identified as the primary active constituents. The nanoemulsion formulation demonstrated excellent potential for use in the biomedical field, with a small and uniform particle size distribution, a negative zeta potential, and high encapsulation efficiency for the O. basilicum extract. The nanoemulsions exhibited spherical morphology and remained physically stable for up to 6 months. In vitro release studies indicated sustained release of the extract from the nanoemulsion formulation compared to the free extract solution. Furthermore, the developed nanoformulation exhibited enhanced anticancer properties against K562 cells while demonstrating low toxicity in normal cells (HEK293). The O. basilicum extract demonstrated antimicrobial activity against Pseudomonas aeruginosa, Candida albicans, and Staphylococcus epidermidis, with a potential synergistic effect observed when combined with the nanoemulsion. These findings contribute to the understanding of the constituents and potential applications of O. basilicum extract and its nanoemulsion formulation in various fields, including healthcare and pharmaceutical industries. Further optimization and research are necessary to maximize the efficacy and antimicrobial activity of the extract and its nanoformulation. RESEARCH HIGHLIGHTS: This study characterized the constituents of O. basilicum extract and assessed the physicochemical properties and stability of its nanoemulsion formulation. The O. basilicum extract contained 22 components, with Caryophyllene and Naringenin identified as the primary active constituents. The nanoemulsion formulation demonstrated excellent potential for biomedical applications, with sustained release of the extract, low toxicity, and enhanced anticancer and antimicrobial properties. The findings contribute to the understanding of the potential applications of O. basilicum extract and its nanoemulsion formulation in healthcare and pharmaceutical industries, highlighting the need for further optimization and research.

Comprehensive analysis of cationic dye removal from synthetic and industrial wastewater using a semi-natural curcumin grafted biochar/poly acrylic acid composite hydrogel

Polymer composites offer a tailored framework as an exceptional candidate for water treatment due to their tunable chemical structure, porous 3D architecture, physiochemical stability, accessibility, pH-sensitivity and ease of use. In this study, curcumin-engineered biochar is embedded into a cross-linked polyacrylic acid hydrogel matrix using in situ polymerization for developing a semi-natural adsorbent for the removal of cationic dye from an aqueous solution. The physicochemical features of the generated composite hydrogel are significantly influenced by the implementation of curcumin-grafted biochar into the polyacrylic acid substrate. Comprehensive characteristic approaches were employed to explore all aspects of the adsorbent's properties, especially its removal efficacy. The methodical adsorption study was accomplished by monitoring dynamic factors such as pH, adsorbent content, time frame, and initial dye concentration. The presence of the porous aromatized structure of biochar, active oxygen-enrich functional groups (carboxyl, hydroxyl, keto, enol, ether) coupled with the conjugated curcumin structure facilitate the effective establishment of hydrogen bonds, electrostatic interactions, π-π interactions, electron donor-acceptor and charge-assisted H-bonding with the malachite green (MG) and rhodamine B (Rho) molecules. The highest adsorption capacities of MG and Rho reached 521 mg g-1 and 741 mg g-1 respectively, in the range of neutral pH, considering their molecular nature, functionalities, and unique adsorption mechanisms. The isothermal modeling was carried out with Henry, Langmuir, Jovanovic, Freundlich, Temkin, and Koble-Corrigan models to determine the adsorption system. Additionally, the kinetic data were assessed with Bangham, pseudo-first-order, pseudo-second-order, intra-particle, and liquid film diffusion models to ascertain the rate-limiting phase. The Koble-Corrigan and Langmuir isotherm models (R2 > 0.997) as well as pseudo-second-order (R2 > 0.998) and Elovich (R2 = 0.983 and 0.995) kinetics models provide a substantial level of concordance with empirical findings. The analysis of non-linear diffusion models revealed that the Bangham (R2 > 0.995) pore and liquid film diffusion (R2 > 0.960) models has major influence on the rate of the adsorption procedure. The binary adsorption test demonstrated higher efficacy of the synthesized adsorbent in the removal of malachite as compared to rhodamine. This study sheds light on the design of a cost-effective semi-natural polymeric composite for treating dye-polluted wastewaters, a major milestone toward environmental and ecological sustainability.

RETRACTED: Impact of High-Frequency Traveling-Wave Magnetic Fields on Low-Conductivity Liquids: Investigation and Potential Applications in the Chemical Industry

High-frequency traveling-wave magnetic fields refer to alternating magnetic fields that propagate through space in a wave-like manner at high frequencies. These magnetic fields are characterized by their ability to generate driving forces and induce currents in conductive materials, such as liquids or metals. This article investigates the application and approaches of a unique form of high-frequency traveling-wave magnetic fields to low-conductivity liquids with conductivity ranging from 1 to 102 S/m. Experiments were conducted using four representative electrolytic solutions commonly employed in the chemical industry: sulfuric acid (H2SO4), sodium hydroxide (NaOH), sodium chloride (NaCl), and ionic liquid ([Bmim]BF4). The investigation focuses on the impact of high-frequency magnetic fields on these solutions at the optimal operating point of the system, considering the effects of Joule heating. The findings reveal that the high-frequency traveling magnetic field exerts a significant volumetric force on all four low-conductivity liquids. This technology, characterized by its non-contact and pollution-free nature, high efficiency, large driving volume, and rapid driving speeds (up to several centimeters per second), also provides uniform velocity distribution and notable thermal effects. It holds considerable promise for applications in the chemical industry, metallurgy, and other sectors where enhanced three-phase transfer processes are essential.

Mo-based MXenes: Synthesis, properties, and applications

Ti-MXene allows a range of possibilities to tune their compositional stoichiometry due to their electronic and electrochemical properties. Other than conventionally explored Ti-MXene, there have been ample opportunities for the non-Ti-based MXenes, especially the emerging Mo-based MXenes. Mo-MXenes are established to be remarkable with optoelectronic and electrochemical properties, tuned energy, catalysis, and sensing applications. In this timely review, we systematically discuss the various organized synthesis procedures, associated experimental tunning parameters, physiochemical properties, structural evaluation, stability challenges, key findings, and a wide range of applications of emerging Mo-MXene over Ti-MXenes. We also critically examined the precise control of Mo-MXenes to cater to advanced applications by comprehensively evaluating the summary of recent studies using artificial intelligence and machine learning tools. The critical future perspectives, significant challenges, and possible outlooks for successfully developing and using Mo-MXenes for various practical applications are highlighted.

Modeling Cu removal from aqueous solution using sawdust based on response surface methodology

Copper (Cu), as one of the heavy metals widely used in industrial and agricultural activities, has a fundamental role in the pollution of water resources. Therefore, removing Cu from the aqueous solutions is considered an important challenge in the purification of water resources. Thus, in this study, sawdust with a diameter of 260-600 μm was used to remove Cu from the aqueous solutions. At first, sawdust was washed using distilled water and dried at laboratory temperature. Cu absorption experiments in closed conditions were performed based on the central composite design (CCD) model and with a range of initial Cu concentrations equal to 1-25 mgl-1. The amount of changes for other variables, including pH, time, and amount of sawdust, was equal to 2-10, 5-185 (min), and 5-25 (gl-1), respectively. After the completion of each test, the remaining Cu concentration in the solution was measured using atomic absorption, and the percentage of Cu removed was determined from the difference between the initial and final concentrations. The results showed that the CCD model has a favorable ability to predict Cu removal from the aqueous solutions (R2=0.90 and RSME=3.34%). Based on the Pareto analysis, contact time, the amount of sawdust, pH, and the Cu concentration had the most significant effect on removing Cu from the solution. Contact time, amount of sawdust, and pH were directly related, and the amount of dissolved Cu was proportional to the removal of Cu from the solution. Therefore, sawdust is desirable as a natural adsorbent, and the removal efficiency of Cu from solutions with low Cu concentration is very high (94%). In this regard, it is advised to use sawdust in the process of targeting Cu and heavy metals due to its low cost and availability.

Synthesis and characterization of titanium dioxide nanoparticles from Bacillus subtilis MTCC 8322 and its application for the removal of methylene blue and orange G dyes under UV light and visible light

Over the last decade there has been a huge increase in the green synthesis of nanoparticles. Moreover, there is a continuous increase in harnessing the potential of microorganisms for the development of efficient and biocompatible nanoparticles around the globe. In the present research work, investigators have synthesized TiO2 NPs by harnessing the potential of Bacillus subtilis MTCC 8322 (Gram-positive) bacteria. The formation and confirmation of the TiO2 NPs synthesized by bacteria were carried out by using UV-Vis spectroscopy, Fourier transforms infrared (FT-IR), X-ray diffraction (XRD), scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDX/EDS). The size of the synthesized TiO2 NPs was 80-120 nm which was spherical to irregular in shape as revealed by SEM. FTIR showed the characteristic bands of Ti-O in the range of 400-550 cm-1 and 924 cm-1 while the band at 2930 cm-1 confirmed the association of bacterial biomolecules with the synthesized TiO2 NPs. XRD showed two major peaks; 27.5° (rutile phase) and 45.6° (anatase phase) for the synthesized TiO2 NPs. Finally, the potential of the synthesized TiO2 NPs was assessed as an antibacterial agent and photocatalyst. The remediation of Methylene blue (MB) and Orange G (OG) dyes was carried out under UV- light and visible light for a contact time of 150-240 min respectively. The removal efficiency for 100 ppm MB dye was 25.75% and for OG dye was 72.24% under UV light, while in visible light, the maximum removal percentage for MB and OG dye was 98.85% and 80.43% respectively at 90 min. Moreover, a kinetic study and adsorption isotherm study were carried out for the removal of both dyes, where the pseudo-first-order for MB dye is 263.269 and 475554.176 mg/g for OG dye. The pseudo-second-order kinetics for MB and OG dye were 188.679 and 1666.667 mg/g respectively. In addition to this, the antibacterial activity of TiO2 NPs was assessed against Bacillus subtilis MTCC 8322 (Gram-positive) and Escherichia coli MTCC 8933 (Gram-negative) where the maximum zone of inhibition in Bacillus subtilis MTCC 8322 was about 12 mm, and for E. coli 16 mm.

Construction and characterization of a magnetic nanoparticle-supported Cu complex: a stable and active nanocatalyst for synthesis of heteroaryl-aryl and di-heteroaryl sulfides

Diaryl and di-heteroaryl sulfides exist in the structure of many drugs and important biological compounds, also these compounds are well-known in medicinal chemistry due to important biological and pharmaceutical activities. Therefore, the development of novel, ecofriendly and efficient catalytic systems for the preparation of diaryl and di-heteroaryl sulfides is a very attractive and important challenge in organic synthesis. In this attractive methodology, we wish to introduce Fe3O4-supported 3-amino-4-mercaptobenzoic acid copper complex (Fe3O4@AMBA-CuI) nanomaterials as a novel and efficient magnetically recoverable catalyst for the preparation of heteroaryl-aryl and di-heteroaryl sulfides with high yields through reaction of heteroaryl halides with aryl or heteroaryl boronic acids and S8 as the sulfur source under ecofriendly conditions. This catalytic system was very efficient and practical for a diverse range of heteroaryl substrates including benzothiazole, benzoxazole, benzimidazole, oxadiazole, benzofuran, and imidazo[1,2-a]pyridine, because the desired diaryl and di-heteroaryl sulfides were prepared with high yields. The reusability-experiments revealed that the Fe3O4@AMBA-CuI nanocatalyst can be magnetically separated and reused at least six times without a significant decrease in its catalytic activity. VSM and ICP-OES analyses confirmed that despite using the Fe3O4@AMBA-CuI nanocatalyst 6 times, the magnetic properties and stability of the catalyst were still maintained. Although all the obtained heteroaryl-aryl and di-heteroaryl sulfide products are known and previously reported, the synthesis of this number of heteroaryl-aryl and di-heteroaryl sulfides has never been reported by any previouse methods.

Nutrient sequestration and lipid production potential of Chlorella vulgaris under pharmaceutical wastewater treatment: experimental, optimization, and prediction modeling studies

The efficient management and treatment of pharmaceutical industry wastewater (PIWW) have become a serious environmental issue due to its high toxicity. To overcome this problem, the present study deals with the phycoremediation of PIWW using Chlorella vulgaris microalga isolated from the Ganga River at Haridwar, India. For this, response surface methodology (RSM) and artificial neural network (ANN) tools were used to identify the best reduction of total phosphorus (TP) and total Kjeldahl's nitrogen (TKN) based pollutants along with the lipid production efficiency of C. vulgaris. Three different concentrations of pharmaceutical wastewater (0, 50, and 100%), operating temperatures (20, 25, and 30 °C), and light intensity (2000, 3000, and 4000 lx) were used to design the phycoremediation experiments having 6:18 h of dark/light period and reactor functional volume of 15L. Findings revealed that C. vulgaris was good enough to remove maximum TP (90.35%), TKN (83.55%) along 20.88% of lipid yield at 25.62 °C temperature, 60.73% PIWW concentration, and 4000 lx of light intensity, respectively. Based on the model performance and validation results, ANN showed more accuracy as compared to the RSM tool. Therefore, the findings of this study showed that C. vulgaris is capable of treating PIWW efficiently along with significant production of lipid content which can further be used in various applications including biofuel production.

Synthesis and characterizations of super adsorbent hydrogel based on biopolymer, Guar Gum-grafted-Poly (hydroxyethyl methacrylate) (Gg-g-Poly (HEMA)) for the removal of Bismarck brown Y dye from aqueous solution

Chemical modification of guar gum was done by graft copolymerization of monomer hydroxyethyl methacrylate (HEMA) using azobisisobutyronitrile (AIBN) as initiator. Optimal reaction parameters were settled by varying one reaction condition and keeping the other constant. The optimum reaction conditions worked out were solvent system: binary, [H2O] = 15.00 mL, [acetone] = 5.00 mL, [HEMA] = 82.217× 10-2 mol/L, [AIBN] = 3.333 × 10-2 mol/L, reaction time = 3 h, reaction temperature = 60 °C on to 1.00 g guar gum with Pg = 1694.6 and %GE = 68,704.152. Pure guar gum polymer and grafts were analyzed by several physicochemical investigation techniques like FTIR, SEM, XRD, EDX, and swelling studies. Percent swelling of the guar gum polymer and grafts was investigated at pH 2.2, 7.0, 7.4 and 9.4 concerning time. The finest yield of Ps was recorded at pH 9.4 with time 24 h for graft copolymer. Guar gum and grafted samples were explored for the sorption of toxic dye Bismarck brown Y from the aqueous solution with respect to variable contact time, pH, temperature and dye concentration so as to investigate the stimuli responsive sorption behaviour. Graft copolymers showed better results than guar gum with percent dye uptake (Du) of 97.588 % in 24 h contact time, 35 °C temperature, 9.4 pH at 150.00 ppm dye feed concentration as compared to Guar gum which only showed 85.260 % dye uptake at alike dye fed concentration. The kinetic behaviour of the polymeric samples was evaluated by applying many adsorption isotherms and kinetic models. The value of 1/n was between 0 → 1 showing that there was physisorption of the BB dye that took place on the surface of the polymers. Thermodynamics of BB Y adsorption onto hydrogels was investigated concerning the Van't Hoff equation. -∆G° values obtained from the curve proved the spontanity of the process. Within the context of adsorption efficiency, an investigation was conducted to examine the process of sorption of Bismarck brown Y dye from aqueous solutions. The graft copolymers demonstrated remarkable adsorption abilities, achieving a dye uptake (Du) of 97.588 % over a 24-h period at a temperature of 35 °C, pH level of 9.4, and a dye concentration of 150.00 ppm. The raised adsorption capacity was additionally corroborated by the application of several adsorption isotherms and kinetic models, which indicated that physisorption is the prevailing process/mechanism. Additionally, the thermodynamic research, utilising the Van't Hoff equation, validated the spontaneity of the adsorption phenomenon, as evidenced by the presence of a negative ∆G° values. The thermodynamic analysis revealed herein establishes a strong scientific foundation for the effectiveness of adsorbent composed of graft copolymers based on guar gum. The research conclude the efficiency of the guar gum based grafted copolymers for the water remediation as efficient adsorbents. The captured dye can be re-utilised and the hydrogels can be used for the same purpose in number of cycles.

Solubilization of chitosan in biologically relevant solvents by a low-temperature solvent-exchange method for developing biocompatible chitosan materials

Chitosan has great potential for biomedical applications. However, the intractable solubility of chitosan is a major bottleneck hampering its utilization. In this work, we report a low-temperature solvent-exchange method to solubilize chitosan in biologically relevant solvents (bio-solvents) including water, salines, and cell culture media. Chitosan was firstly dissolved in ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate (EMIM Ac). The chitosan/IL solution was then dialyzed against bio-solvents at 4 °C, during which a solvent exchange process took place. At the end of 24 h dialysis, aqueous chitosan pseudosolutions formed. Low temperature is found to be crucial for efficient solubilization of chitosan during the solvent exchange process. Increasing temperature to 50 °C leads to the formation of solid chitosan hydrogel. Chitosan in the water-based pseudosolution presents as positively charged particles. The pseudosolution shows a high positive zeta potential of about +52.6 mV and good colloidal stability. The water-based pseudosolutions with different amounts of chitosan contents exhibit the rheological features of weak liquid gels. By using these pseudosolutions, the fabrication of various chitosan materials is realized readily. Both chitosan pseudosolution and its downstream products are highly biocompatible. In this strategy, using IL as a solvent-medium and processing a low-temperature solvent exchange are the two key parameters to solubilize chitosan.

Numerical simulation studies of the new quaternary MAX phase as future engineering applications: The case study of the Nb2ScAC2 (A = Al, Si) compounds

Recently, MAX phases have attained considerable technological interest owing to their two inherent properties metallic and ceramic properties. This study extensively examined Nb2ScAC2 MAX phases using DFT, to assess the structural, mechanical, electronic, and Thermal characteristics. Firstly, the stability of these two compounds was confirmed through the formation energy, elastic constants (Cij), and phonon band structure, which confirmed their thermodynamic, mechanical, and dynamical stability. The optimized lattice parameters of these compounds were examined and then utilized to calculate the physical properties of the Nb2ScAC2 compound. Our compounds are brittle due to their Pugh's ratio of less than 1.75. The covalent bonding of the structure revealed by the Poisson ratio is less than 0.25 for the two compounds. The Nb2ScAC2 material is anisotropic, and Nb2ScAlC2 is harder than Nb2ScSiC2.The metallic character of the materials was affirmed by the electronic band structure analysis. Calculated thermal properties such as Debye temperature and minimum and lattice thermal conductivity reveal that both compounds have the potential to enhance their deployment in thermal barrier coating materials. On the other hand, the high melting temperatures indicate that our compounds could potentially be utilized in demanding or severe conditions. Finally, the thermodynamic characteristics, comprising the isochoric heat capacity (Cv) and Debye temperature (ϴD) were analyzed subjected to high temperatures and pressures. The optical constants such as real and imaginary parts of the dielectric function, refractive index and reflectivity, are investigated. The current study recognizes these two compounds as promising candidates for utilization in modern technologies and diverse industries.

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

A review of novel methods for Diuron removal from aqueous environments

Runoff from intensive agriculture, which contains many sources of pollutants, including herbicides, for instance, Diuron, has threatened the environment and human health. The intrusion of these toxins into water sources poses a serious challenge to human society, and the rising release of these toxins has always been of concern to water researchers. The consequences of the release of these toxins into water sources are destructive and debilitating to human life. Today, the contamination of surface water and wastewater by pesticide residues, especially from agricultural activities and pesticide factories, has grown significantly. One of the pesticides commonly applied around the world is Diuron. There are various techniques for removing Diuron, the most important of which are adsorption and advanced oxidation. This review presents the characteristics, mechanisms, and emerging methods of removing Diuron. The use of absorbents, such as sludge-derived modified biochar (SDMBC600) and bottom ash waste (BAW-200), is discussed in detail. Additionally, the main features, benefits, and limitations of new technologies like hydrodynamic cavitation are enumerated. The effectiveness of novel adsorbents in Diuron removal is also discussed.

Microbial synthesis of titanium dioxide nanoparticles and their importance in wastewater treatment and antimicrobial activities: a review

Nanotechnology (NT) and nanoparticles (NPs) have left a huge impact on every field of science today, but they have shown tremendous importance in the fields of cosmetics and environmental cleanup. NPs with photocatalytic effects have shown positive responses in wastewater treatment, cosmetics, and the biomedical field. The chemically synthesized TiO2 nanoparticles (TiO2 NPs) utilize hazardous chemicals to obtain the desired-shaped TiO2. So, microbial-based synthesis of TiO2 NPs has gained popularity due to its eco-friendly nature, biocompatibility, etc. Being NPs, TiO2 NPs have a high surface area-to-volume ratio in addition to their photocatalytic degradation nature. In the present review, the authors have emphasized the microbial (algae, bacterial, fungi, and virus-mediated) synthesis of TiO2 NPs. Furthermore, authors have exhibited the importance of TiO2 NPs in the food sector, automobile, aerospace, medical, and environmental cleanup.

Antibacterial activity and DNA interaction of triazine iron and ruthenium complexes: spectroscopic, voltammetric and theoretical studies

The 2,4,6-tris(2-pyridyl)-1,3,5-triazine (tptz), [Ru(μ-tptz)2]Cl2 and [Fe(μ-tptz)2]Cl2, complexes containing Ru (1) and Fe (2) are created. Using electronic absorption spectroscopy, fluorescence spectroscopy, circular dichroism spectroscopy, viscosity measurement and electrochemistry, as well as two complexes with Fish Salmon DNA (FS-DNA), the binding interactions of these complexes were investigated. According to binding assays, complexes bind to DNA through a mild intercalation mechanism, most likely via the DNA helix's base pairs being intercalated by the tptz ligand. Additionally, complex (2) is more capable of binding than complex (1). The electrochemical method offers a quick and easy way to determine the binding constant (Kb). The antibacterial performance of these complexes versus Gram-positive and Gram-negative bacteria was examined using the zone of inhibition test, MIC, and MBC method, and the results revealed that complex (2) exhibits strong antibacterial activity against these bacteria. The outcomes of this investigation will help in understanding DNA interaction mechanisms as well as the creation of a prospective one. Additionally, the density functional theory (DFT) computation included probes of DNA structure and conformation as well as potential pharmacological regulators for particular disorders to fully explain the experimental results.

UV and solar-based photocatalytic degradation of organic pollutants from ceramics industrial wastewater by Fe-doped ZnS nanoparticles

UV and solar-based photocatalytic degradation of 2,4-dichlorophenol (2,4-DCP) as an organic contaminant in ceramics industry wastewater by ZnS and Fe-doped ZnS NPs was the focus of this research. Nanoparticles were prepared using a chemical precipitation process. The cubic, closed-packed structure of undoped ZnS and Fe-doped ZnS NPs was formed in spherical clusters, according to XRD and SEM investigations. According to optical studies, the optical band gaps of pure ZnS and Fe-doped ZnS nanoparticles are 3.35 and 2.51 eV, respectively, and Fe doping increased the number of carriers with high mobility, improved carrier separation and injection efficiency, and increased photocatalytic activity under UV or visible light. Doping of Fe increased the separation of photogenerated electrons and holes and facilitated charge transfer, according to electrochemical impedance spectroscopy investigations. Photocatalytic degradation studies revealed that in the present pure ZnS and Fe-doped ZnS nanoparticles, 100% treatment of 120 mL of 15 mg/L phenolic compound was obtained after 55- and 45-min UV-irradiation, respectively, and complete treatment was attained after 45 and 35-min solar light irradiation, respectively. Because of the synergistic effects of effective surface area, more effective photo-generated electron and hole separation efficiency, and enhanced electron transfer, Fe-doped ZnS demonstrated high photocatalytic degradation performance. The study of Fe-doped ZnS's practical photocatalytic treatment capability for removing 120 mL of 10 mg/L 2,4-DCP solution made from genuine ceramic industrial wastewater revealed Fe-doped ZnS's excellent photocatalytic destruction of 2,4-DCP from real industrial wastewater.

The ability of twisted nanographene for removal of Pb2+, Hg2+ and Cd2+ ions from wastewater: Computational study

CONTEXT

Heavy metal ion removal from wastewater has become a global concern due to its extensive negative effects on human health and the environment. The density functional theory is employed to investigate the possibility of removing Pb²⁺, Hg²⁺, and Cd²⁺ ions from wastewater using nano-graphene. Researchers have shown that NG can efficiently remove heavy metals from media. Additionally, it was shown that the adsorption of Pb²⁺, Hg²⁺, and Cd²⁺ ions might reduce the large pristine NG (HOMO-LUMO) gap.

METHODS

HSE06 may accurately represent NG electrical characteristics. The DFT-D3 method was also used to account for Van der Waals interactions in the present study. The results demonstrated that charge transfer and binding energy remained greater in cation-NG systems with greater electron transfer rates. Pb²⁺, Hg²⁺, and Cd²⁺ adsorption results indicated that E_gap was significantly reduced by 68%, 15%, and 21%, respectively. The Pb²⁺@NG complex exhibited the strongest oscillator strength. This may be explained by the enormous occupation number difference between the 2px orbital of the C atoms and the 6 s orbital of the Pb²⁺ cations. The greater E_bin value of Pb²⁺@NG is consistent with the increased predicted redshifts (199 nm). DFT (hybrid functional HSE06) studies that rely on time showed that the relevant complexes have "ligand-to-metal charge transfer" excitations. In general, it was found that Pb²⁺@NG had the greatest k value, binding energy, redshifts, and charge transfer rate among the complexes. The theoretical insights of this study may influence experimental efforts to identify NG-based compounds that are effective and efficient at removing pollutants from wastewater.

Fabrication of polypyrrole gas sensor for detection of NH3 using an oxidizing agent and pyrrole combinations: Studies and characterizations

The organic polymer known as Polypyrrole (Ppy) is synthesized when pyrrole monomers are polymerized. Excellent thermal stability, superior electrical conductivity, and environmental stability are all characteristics of Polypyrrole. Chemical oxidative polymerization was used to synthesize Ppy using Ferric chloride (FeCl₃) as an oxidizing agent and surfactant CTAB in aqueous solution. Oxidant (FeCl₃) to pyrrole varied in different molar ratios (2, 3, 4 and 5). It was found that increasing this ratio up to 4 increases PPy's conductivity. XRD, FTIR, and SEM were used to characterize Ppy. The conductive nature of Ppy was studied by I-V characteristics. The best conductive polymer is studied for the NH₃ gas response.

Fe3O4@ABA-aniline-CuI nanocomposite as a highly efficient and reusable nanocatalyst for the synthesis of benzothiazole-sulfide aryls and heteroaryls

Studying diaryl sulfides and benzothiazoles is important in organic synthesis because numerous natural and medicinal products contain these scaffolds. Over the past few years, research on the synthesis of compounds containing benzothiazole-sulfide aryls, as important biological molecules, has received significant attention. Multicomponent reactions are the most popular strategy for performing difficult reactions and the synthesis of complexed molecules such as benzothiazole-sulfide aryls. In this work, CuI was successfully immobilized on the surface of magnetic Fe₃O₄ nanoparticles modified with aniline and 4-aminobenzoic acid [Fe₃O₄@ABA-Aniline-CuI nanocomposite] and its catalytic activity was investigated in the preparation of a broad range of benzothiazole-sulfide aryls and heteroaryls through the one-pot three-component reactions of 2-iodoaniline with carbon disulfide and aryl or heteroaryl iodides in the presence of KOAc as base in PEG-400 as solvent. TEM and SEM images revealed that the shape of the Fe₃O₄@ABA-Aniline-CuI particles is spherical and the size of the particles is approximately between 12-25 nanometers.

Metals (Ga, In) decorated fullerenes as nanosensors for the adsorption of 2,2-dichlorovinyldimethylphosphate agrochemical based pollutant

Owing to the fact that the use of 2,2-dichlorovinyldimethylphosphate (DDVP) as an agrochemical has become a matter of concern due to its persistence and potential harm to the environment and human health. Detecting and addressing DDVP contamination is crucial to protect human health and mitigate ecological impacts. Hence, this study focuses on harnessing the properties of fullerene (C60) carbon materials, known for their biological activities and high importance, to develop an efficient sensor for DDVP. Additionally, the sensor's performance is enhanced by doping it with gallium (Ga) and indium (In) metals to investigate the sensing and trapping capabilities of DDVP molecules. The detection of DDVP is carefully examined using first-principles density functional theory (DFT) at the Def2svp/B3LYP-GD3(BJ) level of theory, specifically analyzing the adsorption of DDVP at the chlorine (Cl) and oxygen (O) sites. The adsorption energies at the Cl site were determined as - 57.894 kJ/mol, - 78.107 kJ/mol, and - 99.901 kJ/mol for Cl_DDVP@C60, Cl_DDVP@Ga@C60, and Cl_DDVP@In@C60 interactions, respectively. At the O site, the adsorption energies were found to be - 54.400 kJ/mol, - 114.060 kJ/mol, and - 114.056 kJ/mol for O_DDVP@C60, O_DDVP@Ga@C60, and O_DDVP@In@C60, respectively. The adsorption energy analysis highlights the chemisorption strength between the surfaces and the DDVP molecule at the Cl and O sites of adsorption, indicating that the O adsorption site exhibits higher adsorption energy, which is more favorable according to the thermodynamics analysis. Thermodynamic parameters (∆H and ∆G) obtained from this adsorption site suggest considerable stability and indicate a spontaneous reaction in the order O_DDVP@Ga@C60 > O_DDVP@In@C60 > O_DDVP@C60. These findings demonstrate that the metal-decorated surfaces adsorbed on the oxygen (O) site of the biomolecule offer high sensitivity for detecting the organophosphate molecule DDVP.

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

Land subsidence (LS) as a major geological and hydrological hazard poses a major threat to safety and security. The various triggers of LS include intense extraction of aquifer bodies. In this study, we present an LS inventory map of the Daumeghan plain of Iran using 123 LS and 123 non-LS locations which were identified through field survey. Fourteen LS causative factors related to topography, geology, hydrology, and anthropogenic characteristics were selected based on multi-collinearity test. Based on the results, five susceptibility maps were generated employing models and input data. The LS susceptibility models were evaluated and validated using the receiver operating characteristic (ROC) curve and statistical indices. The results indicate that the LS susceptibility maps produced have good accuracy in predicting the spatial distribution of LS in the study area. The result showed that the optimization models BA and GWO were better than the other machine learning algorithm (MLA). In addition, The BA model has 96.6% area under of ROC (AUROC) followed by GWO (95.8%), BART (94.5%), BRT (93.1%), and SVR (92.7%). The LS susceptibility maps formulated in our study can serve as a useful tool for formulating mitigation strategies and for better land-use planning.

Green synthesis of Fe3O4 nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression

The synthesis of metal nanoparticles using green chemistry methods has gained significant attention in the field of landscape enhancement. Researchers have paid close attention to the development of very effective green chemistry approaches for the production of metal nanoparticles (NPs). The primary goal is to create an environmentally sustainable technique for generating NPs. At the nanoscale, ferro- and ferrimagnetic minerals such as magnetite exhibit superparamagnetic properties (Fe3O4). Magnetic nanoparticles (NPs) have received increased interest in nanoscience and nanotechnology due to their physiochemical properties, small particle size (1-100 nm), and low toxicity. Biological resources such as bacteria, algae, fungus, and plants have been used to manufacture affordable, energy-efficient, non-toxic, and ecologically acceptable metallic NPs. Despite the growing demand for Fe3O4 nanoparticles in a variety of applications, typical chemical production processes can produce hazardous byproducts and trash, resulting in significant environmental implications. The purpose of this study is to look at the ability of Allium sativum, a member of the Alliaceae family recognized for its culinary and medicinal benefits, to synthesize Fe3O4 NPs. Extracts of Allium sativum seeds and cloves include reducing sugars like glucose, which may be used as decreasing factors in the production of Fe3O4 NPs to reduce the requirement for hazardous chemicals and increase sustainability. The analytic procedures were carried out utilizing machine learning as support vector regression (SVR). Furthermore, because Allium sativum is widely accessible and biocompatible, it is a safe and cost-effective material for the manufacture of Fe3O4 NPs. Using the regression indices metrics of root mean square error (RMSE) and coefficient of determination (R2), the X-ray diffraction (XRD) study revealed the lighter, smoother spherical forms of NPs in the presence of aqueous garlic extract and 70.223 nm in its absence. The antifungal activity of Fe3O4 NPs against Candida albicans was investigated using a disc diffusion technique but exhibited no impact at doses of 200, 400, and 600 ppm. This characterization of the nanoparticles helps in understanding their physical properties and provides insights into their potential applications in landscape enhancement.

APPLICATION OF MICELLAR LIQUID CHROMATOGRAPHY TO MODEL ECOTOXICITY OF PESTICIDES. COMPARISON WITH IMMOBILIZED ARTIFICIAL MEMBRANE CHROMATOGRAPHY AND N-OCTANOL-WATER PARTITIONING

The potential of Micellar Liquid Chromatography (MLC) to model ecotoxicological endpoints for a series of pesticides was investigated. To exploit the flexibility in MLC conditions, different surfactants were employed and retention mechanism was tracked and compared to Immobilized Artificial Membrane (IAM) chromatographic retention and n-octanol- water partitioning, logP. Neutral polyoxyethylene (23) lauryl ether (Brij-35), anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) were used in presence of PBS at pH=7.40 and acetonitrile as organic modifier when necessary. Similarities/ dissimilarities between MLC retention and IAM or logP were investigated by Principal Component Analysis (PCA) and Liner Solvation Energy Relationships (LSER). LSER revealed that hydrogen bonding acidity is the most important factor for differentiation between MLC and IAM or logP. The impact of hydrogen bonding is exemplified in the relationships of MLC retention factors with IAM or logP, which necessitate the inclusion of a relevant descriptor. PCA further revealed that MLC retention factors are clustered together with IAM indices and logP within a broader ellipse formed by ecotoxicological endpoints, involving LC₅₀/ EC₅₀ values of six aquatic organisms namely Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values of Honey Bee, thus justifying their use to construct relevant models. Satisfactory specific models for individual organisms, as well as general fish models, were obtained, in most cases, upon combination of MLC retention factors with Molecular Weight (MW) or/ and hydrogen bond parameters. All models were evaluated and compared to previously reported IAM and logP based models using an external validation data set. Predictions with Brij-35 and SDS based models were comparable, although slightly inferior than those obtained with IAM, while they were in all cases better than those obtained with logP. CTAB led to a satisfactory prediction model for Honey Bee, but it was found less suitable for aquatic organisms.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., K_XAD-A) are indispensable to obtain POPs concentration, and the K_XAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of K_XAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting K_XAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²_adj and Q²_ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²_adj = 0.991, Q²_ext = 0.935, RMSE_tra = 0.271 and RMSE_ext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting K_XAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.

Key difference between transition state stabilization and ground state destabilization: increasing atomic charge densities before or during enzyme–substrate binding

The origin of the enormous catalytic power of enzymes has been extensively studied through experimental and computational approaches. Although precise mechanisms are still subject to much debate, enzymes are thought to catalyze reactions by stabilizing transition states (TSs) or destabilizing ground states (GSs). By exploring the catalysis of various types of enzyme–substrate noncovalent interactions, we found that catalysis by TS stabilization and the catalysis by GS destabilization share common features by reducing the free energy barriers (ΔG^‡s) of reactions, but are different in attaining the requirement for ΔG^‡ reduction. Irrespective of whether enzymes catalyze reactions by TS stabilization or GS destabilization, they reduce ΔG^‡s by enhancing the charge densities of catalytic atoms that experience a reduction in charge density between GSs and TSs. Notably, in TS stabilization, the charge density of catalytic atoms is enhanced prior to enzyme–substrate binding; whereas in GS destabilization, the charge density of catalytic atoms is enhanced during the enzyme–substrate binding. Results show that TS stabilization and GS destabilization are not contradictory to each other and are consistent in reducing the ΔG^‡s of reactions. The full mechanism of enzyme catalysis includes the mechanism of reducing ΔG^‡ and the mechanism of enhancing atomic charge densities. Our findings may help resolve the debate between TS stabilization and GS destabilization and assist our understanding of catalysis and the design of artificial enzymes.

Transition state stabilization and ground state destabilization utilize the same molecular mechanism when lowering the free energy barriers (ΔG^‡s) of reactions, but differ in achieving the requirement for ΔG^‡ reduction.

Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔG_Fs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔG_Fs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method

The prediction of [Formula: see text] values is one part of the statistical assessment of the modeling of proteins and ligands (SAMPL) blind challenges. Here, we use a molecular graph representation method called Geometric Scattering for Graphs (GSG) to transform atomic attributes to molecular features. The atomic attributes used here are parameters from classical molecular force fields including partial charges and Lennard-Jones interaction parameters. The molecular features from GSG are used as inputs to neural networks that are trained using a "master" dataset comprised of over 41,000 unique [Formula: see text] values. The specific molecular targets in the SAMPL7 [Formula: see text] prediction challenge were unique in that they all contained a sulfonyl moeity. This motivated a set of ClassicalGSG submissions where predictors were trained on different subsets of the master dataset that are filtered according to chemical types and/or the presence of the sulfonyl moeity. We find that our ranked prediction obtained 5th place with an RMSE of 0.77 [Formula: see text] units and an MAE of 0.62, while one of our non-ranked predictions achieved first place among all submissions with an RMSE of 0.55 and an MAE of 0.44. After the conclusion of the challenge we also examined the performance of open-source force field parameters that allow for an end-to-end [Formula: see text] predictor model: General AMBER Force Field (GAFF), Universal Force Field (UFF), Merck Molecular Force Field 94 (MMFF94) and Ghemical. We find that ClassicalGSG models trained with atomic attributes from MMFF94 can yield more accurate predictions compared to those trained with CGenFF atomic attributes.

ClassicalGSG: Prediction of log P using classical molecular force fields and geometric scattering for graphs

This work examines methods for predicting the partition coefficient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic attributes are transformed into index-invariant molecular features using a recently developed method called geometric scattering for graphs (GSG). We call this approach "ClassicalGSG" and examine its performance under a broad range of conditions and hyperparameters. We train ClassicalGSG log P predictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the log P values from four independent test sets. The ClassicalGSG method's performance is compared to a baseline model that employs graph convolutional networks. Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized force field and 2D molecular structures.