Fabrication of thiosemicarbazide-modified biochar/carrageenan composite beads based on Eichhornia crassipes for effective removal of Pb (II) from aqueous medium.

Several biomasses have been applied as environmentally friendly substitutes to produce biochar, which can be utilized to remediate effluents that contain inorganic chemicals. This study applied water hyacinth (Eichhornia crassipes) as a foundation source for the assembly of thiosemicarbazide-modified biochar (BC), which then was modified with potassium carrageenan (KC). Thiosemicarbazide-modified biochar (BC), potassium carrageenan (KC), and thiosemicarbazide-modified biochar/carrageenan composite beads (BKC) were described by several physicochemical methods. The adsorption of Pb (II) onto the three solid adsorbents was investigated under various experimental conditions. The BKC composite beads revealed a surface area of 687.43 m2/g and a mesoporous structure. The best adsorption conditions were found to be 25 min as an equilibrium time, 1.2 g/L of adsorbent dose, and a solution pH of 5 at a temperature of 15 °C. The pseudo-second-order, Elovich kinetic models, Langmuir, and Temkin isotherms were well familiar to the experimental data, inferring that the progression was physical monolayer adsorption onto the homogenous surface. The highest capacity of Pb (II) adsorption onto BKC was 460.45 mg/g at 15 °C. Thermodynamic measurements proved that adsorption was a spontaneous process and endothermic in the case of BC and BKC while exothermic for KC. Furthermore, BKC showed high reusability conditions.

Synthesis and characterization of a novel TiO2@chitosan/alginate nanocomposite sponge for highly efficient removal of As(V) ions from aqueous solutions: Adsorption isotherm, kinetics, experiment and adsorption mechanism optimization using Box-Behnken design.

This research uses a novel TiO2@CSC.Alg composite sponge was created by encasing TiO2 nanoparticles in the natural polymers alginate and chitosan, resulting in a nanocomposite that is both ecologically friendly and biocompatible. Using the generated nanocomposite as a new environmentally friendly adsorbent, As(V) heavy metal ions were effectively removed from aqueous media. The following techniques were used to analyse the physicochemical properties of the obtained materials: pHZPC, FTIR, XRD, BET, SEM, and XPS. Utilizing nitrogen adsorption/desorption isotherms, the TiO2@CSC.Alg composite sponge's textural properties were identified. This revealed a BET surface area of 168.42 m2/g and a total pore volume of 1.18 cc/g, indicating its porous nature and potential for high adsorption capacity. Examine the effects of temperature, pH, dose, and beginning concentration on adsorption. The adsorption characteristics were determined based on equilibrium and adsorption kinetics measurements. The adsorption process was both pseudo-second-order (PSOE) and Langmuir isothermally fit. Chemisorption was the adsorption method since the adsorption energy was 25.45 kJ·mol-1. An endothermic and spontaneous adsorption process was indicated by more metal being absorbed as the temperature increased. The optimal conditions for adsorption were optimized via Box-Behnken design software to be pH of 5 in the solution, a dosage of 0.02 g of the TiO2@CSC.Alg composite sponge per 25 mL, and an arsenate (As(V)) solution the adsorption capacity was 202.27 mg/g are ideal for efficient adsorption. These parameters are critical in achieving the maximum adsorption capacity of the composite sponge for arsenate, which could be beneficial for water purification applications. Utilizing Design-Expert software's response surface methodology (RSM) and Box-Behnken design (BBD), the adsorption process was optimized with the fewest planned tests. After six successive cycles of adsorption and desorption, the adsorbent stability was confirmed by the adsorbent reusability test without any noticeable decrease in removal efficacy. Additionally, it displayed good efficiency, the same XRD and XPS data before and after reuse, and no change in chemical composition.

Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments.

Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a near-infrared (NIR) pulse to rapidly heat a sample, offering an approach for triggering these processes and directly accessing thermally-activated pathways. However, thermal activation inherently increases the disorder of the system under study and, as a consequence, can make quantitative interpretations of structural changes challenging. In this Article, we optimise a deep neural network (DNN) for the instantaneous prediction of Co K-edge X-ray absorption near-edge structure (XANES) spectra. We apply our DNN to analyse T-jump pump/X-ray probe data pertaining to the ligand exchange processes and solvation dynamics of Co2+ in chlorinated aqueous solution. Our analysis is greatly facilitated by machine learning, as our DNN is able to predict quickly and cost-effectively the XANES spectra of thousands of geometric configurations sampled from ab initio molecular dynamics (MD) using nothing more than the local geometric environment around the X-ray absorption site. We identify directly the structural changes following the T-jump, which are dominated by sample heating and a commensurate increase in the Debye-Waller factor.

The Role of Structural Representation in the Performance of a Deep Neural Network for X-Ray Spectroscopy.

An important consideration when developing a deep neural network (DNN) for the prediction of molecular properties is the representation of the chemical space. Herein we explore the effect of the representation on the performance of our DNN engineered to predict Fe K-edge X-ray absorption near-edge structure (XANES) spectra, and address the question: How important is the choice of representation for the local environment around an arbitrary Fe absorption site? Using two popular representations of chemical space-the Coulomb matrix (CM) and pair-distribution/radial distribution curve (RDC)-we investigate the effect that the choice of representation has on the performance of our DNN. While CM and RDC featurisation are demonstrably robust descriptors, it is possible to obtain a smaller mean squared error (MSE) between the target and estimated XANES spectra when using RDC featurisation, and converge to this state a) faster and b) using fewer data samples. This is advantageous for future extension of our DNN to other X-ray absorption edges, and for reoptimisation of our DNN to reproduce results from higher levels of theory. In the latter case, dataset sizes will be limited more strongly by the resource-intensive nature of the underlying theoretical calculations.