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Water management is a critical aspect of maximizing the performance and lifespan of proton exchange membrane fuel cells (PEMFCs). A membrane humidifier is an essential piece of equipment for maintaining PEMFC performance. This study focuses on assessing the in-situ humidification potential of three membranes - Nafion (NR-212), reverse osmosis (RO), and pervaporation (PV) membranes - for use in a planar membrane humidifier. The humidification potential was evaluated based on five different parameters: pressure drop, water recovery ratio, water flux, coefficient of performance, and dew point approach temperature. To create an accurate deep learning model, the study chose flow rate, temperature, humidity, and membrane type and material as the most significant input parameters. The data was first augmented using CTGAN, and the synthetic data was found to be well-correlated with real data, with a similarity score of 0.4805. The optimal deep neural network (DNN) model was created using Bayesian surrogate models, including random forest, Gaussian process regression, and gradient boosting regression trees. The model demonstrated a high level of accuracy, with a correlation coefficient of 0.986 and a mean absolute error of 0.077, 0.22, 0.265, 0.03, and 0.045 for pressure loss, DPAT, WRR, J, and COP, respectively. The model was also validated on unseen experimental data, with a correlation coefficient of 0.94. Finally, the predictions of the deep learning model were analyzed using explainable artificial intelligence (XAI) via the SHAP library. The analysis included dependence plots, embedding plots, partial dependence plots, summary plots, and force plots. When assessing the in-situ humidification performance of planar membrane humidifiers, temperature is the primary input variable that influences the outcome, followed by flow rate and humidity.

[Display omitted] • A novel MIP based pendant was synthesized for lufenuron extraction in real samples. • Various statistical and kinetic approaches were employed to validate its efficacy. • MIP pendant showed excellent selectivity, stability, and reusability. • MIPs pendant exhibited good linearity and high sensitivity for lufenuron. As a key member of benzoylurea insecticides, lufenuron exhibits a strong potential for bioaccumulation in ecosystems. Here, we report the synthesis of a molecularly imprinted polymer (MIP) and its application for selective extraction of lufenuron from agricultural runoff samples. The morphology and thermal stability of the prepared MIP was characterized by SEM and TGA, respectively. The experimental conditions for lufenuron detection were optimized through batch binding assay and response surface methodology (RSM). The MIP showcased a two-fold enhancement in binding capacity of lufenuron (74 mg g−1) relative to non-imprinted polymer (36 mg g−1), when measured through batch binding assay. Using a RSM based on central composite design, the q e value was predicted as 73.6 mg g−1 under optimal experimental conditions (e.g., pH 8). Furthermore, the percentage recovery of lufenuron was estimated as 98.8 ± 1.8% (n = 9). The adsorption kinetics of the MIP followed the pseudo-first-order model with χ2 value of 0.004 (correlation coefficient of 0.99). The limit of detection for lufenuron using MIP-based method was 3 × 10−6 µg mL−1. There was no significant decrease in the performance of MIP against lufenuron over five successive cycles. [ABSTRACT FROM AUTHOR]

[Display omitted] MnO 2 nanostructures with three distinct architectures, namely flower, balk and tube-like, have been synthesized through a single step microwave assisted hydrothermal method at different reaction temperatures (110 °C, 140 °C and 180 °C). The characterization of as prepared MnO 2 samples were performed by means of Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The N 2 adsorption–desorption isotherms revealed the higher specific surface area and porosity of the flower like MnO 2 as compared to balk and tube-like MnO 2. The adsorption behavior of as prepared adsorbents was investigated towards Sr2+ radionuclide. Because of the hierarchal structure and the high surface area (62.64 m2/g), MnO 2 -110 depicted the best Sr2+ adsorption performance with maximum adsorption capacity of 52 mg/g at pH 6 as compared to other MnO 2 morphologies synthesized at 140 °C and 180 °C. The kinetic studies revealed that the adsorption of Sr2+ onto MnO 2 -110 followed the pseudo-first-order model whereas the adsorption equilibrium data obeyed the Freundlich and Sips model. Moreover, the MnO 2 -110 adsorbent reached the steady state quickly (∼10 min) and is capable to bind Sr2+ in slightly acidic to alkaline solutions. [ABSTRACT FROM AUTHOR]

The industrial revolution although has reached its unprecedented altitude benefiting mankind in various aspects yet the resulting pollution is a serious threat to the environment. Dyes are among the major pollutants that contaminate water resources. Hence, a facile synthesis of PANI/MgFe2O4nanoferrite composite and its hybrids as photocatalysts for the degradation of Indigo carmine in aqueous solutions is described. The photocatalysts were designed in situ through self-polymerization of monomer aniline. The catalytic activity of each composite was studied by evaluating its efficiency to photodegrade water soluble carcinogenic indigo carmine dye in aqueous solution. Different parameters like dye concentration, dose of photocatalyst, contact time and pH have been studied to optimize reaction conditions. The FTIR, SEM and XRD studies were carried to determine chemical as well as morphological feature characteristics of nanoferrite composite (MgFe2O4+PANI) and hybrid nanoferrite composites (MFe2O4+ PANI + silica gel). The XRD confirmed the cubic structure of nano ferrite. The SEM image of polyaniline/composite suggested porous surface and highly agglomerated granular shape. The FTIR spectra of polyaniline and its composites evinced the formation of composite possessing spinal ferrite structure. The single coated PANI/MgFe2O4composite presented higher photo-degradation percentage i.e. 97.52% as compared to hybrid nanoferrite composites i.e. 94.36% and hence presented promising results to purify water contaminated with respective dye.

The static failure behavior of innovative wood-based sandwich panels with wood-based core and face sheets made of glass fiber reinforced polymer was investigated. The sandwich panels were subjected to three-point bending and compression tests to determine their strengths and their failure mechanisms. The honeycomb core of medium density fiberboard (MDF) was manufactured to reduce the weight of the whole panel as well as the consumption of wood elements. The hollow portions of the honeycomb core were filled with wheat straw to increase the heat resistance of the whole structure by maintaining a lightweight. The tests showed that the mechanical characteristics of solid MDF structures were high as compared to MDF honeycomb panels. But, MDF honeycomb structures were superior because of their capability to sustain against the load after the failure. Load absorption capabilities and strength of honeycomb sandwich panels filled with wheat straw were little improved. The major fracture modes were transverse shear, longitudinal shear, and delamination. The delamination at core and facing interface was the significant problem which can effectively be reduced by using inserts into the core above face sheets.

The proposed study aimed to investigate the potential use of pristine multi-walled carbon nanotubes (MWCNTs) as nano reinforcement in enhancing mechanical properties of hybrid MWCNT/silica fume cement composites. Dispersion of MWCNTs was facilitated utilizing very fine particles of silica fume which also helped in an improved interfacial bond between MWCNTs and the cement matrix. The MWCNTs dispersion within the hardened cement matrix was qualitatively assessed by Field Emission Scanning Electron Microscope (FESEM) analysis. It was also observed that addition of MWCNTs accelerated the hydration process. The test results showed an increment in compressive strength by 12.4% and reduction in autogenous shrinkage by 8.5% for hybrid MWCNT/silica fume cement composites containing 0.01% MWCNTs (by wt. of binder). However, higher additions (greater than 0.03%) of MWCNTs appeared to have adverse effects on specimens. It was found that properly dispersed MWCNTs filled the fine pores in the cement matrix by providing an additional nucleation site for the formation of calcium silicate hydrate (C-S-H) that results in a denser microstructure, which in turn enhanced the strengths and limited the autogenous shrinkage.

Metal-Organic Framework (MOFs) consist of organic ligands having metal ions at the center that have many properties especially large surface area, polymetallic sites, tunable organic ligands, and porous structure. In this work, a three-dimensional MOF ZIF-67 was decorated over the g-C3N41D hollow tubular structure by a simple precipitation technique. The proposed CoSxco-catalyst was introduced as a free condition in the solution derived from ZIF-67 rather than loaded on the 1D g-C3N4. It is suggested that CoSxoffers an active site for the reduction of water molecules after accepting the photogenerated electrons from the 1D g-C3N4. The composite reveals high hydrogen performance (13911 μmol∙g−1 h−1) in comparison with ZIF-67 (4885 μmol∙g−1 h−1) and h-CN (1229 μmol∙g−1 h−1). Additionally, the composite sample has Quantum efficiency (Q.E) of about 19.5 % for hydrogen evolution reaction (HER) at 420 nm which is significantly greater than the h-CN (1.72 %) and ZIF-67 (6.87 %) sample. The work opened a new avenue in the field of photocatalysis by suggesting noble metal-free and high-efficiency MOF heterojunction.

Hydrogen storage using liquid organic hydrogen carriers (LOHCs) is a promising method. The data sets for hydrogen storage using dibenzyltoluene (DBT) are considered in this study. The important input parameters to predict the hydrogen storage in DBT are temperature, pressure, stirring speed, catalyst dosage, and amount of DBT. In this manuscript, Hydrogen Storage Prediction System Empowered with Machine Learning (HSPSML) is proposed. The three different Artificial Neural Network (ANN) approaches such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient are chosen to predict the hydrogen storage capacities and their results are compared to indicate the optimal approach. The data sets are classified into two classes i.e., low and high. The overall accuracy of the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) are 98.70 % whereas it is 94.87 % for the Levenberg-Marquardt (LM) approach. The accuracy of the LM approach is lower due to the high miss clarification rate of 12.8 % of the low class. The low class accuracy is 100 % in the other two approaches which resulted in the higher overall accuracy of these methods. Therefore, the BR and SCG are found to be the optimal approaches to predicting hydrogen storage capacities.

The aim of this study is to investigate the effect of roughness fabrication methods on the corrosion resistance of Al-5050 alloy. The surfaces having roughness (0.45–6.2 μm) are generated by polishing, machining, and sandblasting. The surfaces are tested in 3.5wt%NaCl aqua solution with the voltage from −0.2 to 1.0 V with a scan rate of 0.1mv/s. Tafel curves are plotted to determine the corrosion current density (Icorr) and the corrosion potential (Ecorr). In addition to this, corrosion behavior is analyzed by the Electrochemical Impedance Spectroscopy (EIS). Before and after corrosion experiments, surface morphology of the samples is analyzed by scanning electron microscope (SEM) equipped with energy dispersive spectroscopy. Also, X-ray Diffraction (XRD) analysis is carried out to investigate the corrosion products present on the surface of the Al-5050 alloy. The results indicate that the corrosion rate is increased with an increase in the surface roughness. The machined max surface shows 21.9% higher corrosion rate compared to the polished surface. Sandblasted surface due to protective layer and homogeneity in surface roughness experiences less corrosion rate than the machined max surface. The larger and deeper grooves in the machined surface lead to severe pitting and higher corrosion. The type of surface roughness plays a significant role in corrosion behavior and cathodic and anodic kinetics.