2D Ni2P/N-doped graphene heterostructure as a Novel electrocatalyst for hydrogen evolution reaction: A computational study.

The main prerequisite for designing electrocatalysts with favorable performance is to examine the links between electronic structural features and catalytic activity. In this work, Ni2P as a model electrocatalyst and one of the most potent catalysts for hydrogen evolution reaction (HER) was utilized to develop various Ni2P and carbon-based (graphene and N-doped graphene) heterostructures. The characteristics of such structures (Ni2P, graphene, N-doped graphene, Ni2P/graphene, and Ni2P/N-doped graphene), including binding energies, the projected density of states (PDOS), band structure, charge density difference, charge transfer, Hirshfeld charge analysis, and minimum-energy path (MEP) towards HER were calculated and analyzed by density functional theory (DFT) approach. The coupling energy values of hybrid systems were correlated with the magnitude of charge transfer. The main factors driving a promising water-splitting reaction were explained by the data of PDOS, band structures, and charge analysis, including the inherent electronegativity of the N species alongside shifting the Fermi level toward the conductive band. It was also shown that a significant drop occurs in the HER energy barrier on Ni2P/graphene compared to the pristine Ni2P due to N doping on the graphene layer in the Ni2P/N-doped graphene heterostructure.

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach.

The AB2 metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB2 alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB2 metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R2, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB2 metal hydrides.

Design of 2D/2D ß-Ni(OH)2/ZnO heterostructures via photocatalytic deposition of nickel for sonophotocatalytic degradation of tetracycline and modeling with three supervised machine learning algorithms.

Due to the expansive use of tetracycline antibiotics (TCs) to treat various infectious diseases in humans and animals, their presence in the environment has created many challenges for human societies. Therefore, providing green and cost-effective solutions for their effective removal has become an urgent need. Here, we will introduce 2D/2D p-n heterostructures that exhibit excellent sonophotocatalytic/photocatalytic properties for water-soluble pollutant removal. In this contribution, for the first time, ß- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (ß-Ni(x)/ZNs) to fabricate 2D/2D p-n heterostructures. The PXRD patterns confirmed the formation of wurtzite phase for ZNs and the hexagonal crystal structure of ß-Ni(OH)2. The FESEM and TEM micrographs showed that the ß-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in ß-Ni(x)/ZNs samples. With the photodeposition of ß-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of ß-Ni(x)/ZNs heterostructures have increased compared to ZNs, which can have a positive effect on the sonophotocatalytic/photocatalytic performance of ZNs. The degradation experiments showed that ß-Ni(0.1)/ZNs and ß-Ni(0.4)/ZNs have the highest degradation percentage in photocatalytic (51 %) and sonophotocatalytic (71 %) degradation of TC, respectively. Finally, the sonophotocatalytic/photocatalytic degradation process of TC was systematically validated through modeling with three powerful and supervised machine learning algorithms, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Stochastic Gradient Boosting (SGB). Five statistical criteria including R2, SAE, MSE, SSE, and RMSE were calculated for model validation. It was observed that the developed SGB algorithm was the most reliable model for predicting the degradation percent of TC. The results revealed that using fabricated 2D/2D p-n heterojunctions (ß-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

Nanoparticles and nanofiltration for wastewater treatment: From polluted to fresh water.

Water pollution poses significant threats to both ecosystems and human health. Mitigating this issue requires effective treatment of domestic wastewater to convert waste into bio-fertilizers and gas. Neglecting liquid waste treatment carries severe consequences for health and the environment. This review focuses on intelligent technologies for water and wastewater treatment, targeting waterborne diseases. It covers pollution prevention and purification methods, including hydrotherapy, membrane filtration, mechanical filters, reverse osmosis, ion exchange, and copper-zinc cleaning. The article also highlights domestic purification, field techniques, heavy metal removal, and emerging technologies like nanochips, graphene, nanofiltration, atmospheric water generation, and wastewater treatment plants (WWTPs)-based cleaning. Emphasizing water cleaning's significance for ecosystem protection and human health, the review discusses pollution challenges and explores the integration of wastewater treatment, coagulant processes, and nanoparticle utilization in management. It advocates collaborative efforts and innovative research for freshwater preservation and pollution mitigation. Innovative biological systems, combined with filtration, disinfection, and membranes, can elevate recovery rates by up to 90%, surpassing individual primary (<10%) or biological methods (≤50%). Advanced treatment methods can achieve up to 95% water recovery, exceeding UN goals for clean water and sanitation (Goal 6). This progress aligns with climate action objectives and safeguards vital water-rich habitats (Goal 13). The future holds promise with advanced purification techniques enhancing water quality and availability, underscoring the need for responsible water conservation and management for a sustainable future.

Screen-printed Sn-doped TiO2 nanoparticles for photocatalytic dye removal from wastewater: A technological perspective.

TiO2 is widely used as a photocatalyst with a wide band gap, which limited its application. Ion doping and formulating a high-quality screen-printing paste enhance its features. However, the printability of objects for advanced application seems essential nowadays. In this research, the Sn-doped TiO2 nanoparticles were prepared through a sol-gel method followed by calcination at various temperatures of 450 °C, 550 °C, 650 °C, 750 °C, and 850 °C. Screen-printing pastes were prepared with 18 wt% of the synthesized Sn-doped TiO2 nanoparticles to evaluate photocatalytic activity. Finally, the prepared paste with optimum nanoparticle concentration was screen printed onto the microscope glass slides at various printing times (1, 3, and 5 runs) and annealed at 500 °C temperature to investigate the thickness of printed Sn-doped TiO2 nanoparticles effect. The photocatalytic activity and crystal structure of nano Sn-doped-TiO2 were characterized using photoluminescence (PL) spectroscopy and X-ray diffraction (XRD). Transmission electron microscopy (TEM) and scanning electron microscope (SEM) analyses were conducted to investigate the size and morphology of the prepared nanoparticles, respectively. The highest photocatalytic activity for the degradation of methylene blue was obtained at the calcination temperature of 450 °C.

Wastewater reuse in agriculture: Prospects and challenges.

Sustainable water recycling and wastewater reuse are urgent nowadays considering water scarcity and increased water consumption through human activities. In 2015, United Nations Sustainable Development Goal 6 (UN SDG6) highlighted the necessity of recycling wastewater to guarantee water availability for individuals. Currently, wastewater irrigation (WWI) of crops and agricultural land appears essential. The present work overviews the quality of treated wastewater in terms of soil microbial activities, and discusses challenges and benefits of WWI in line with wastewater reuse in agriculture and aquaculture irrigation. Combined conventional-advanced wastewater treatment processes are specifically deliberated, considering the harmful impacts on human health arising from WWI originating from reuse of contaminated water (salts, organic pollutants, toxic metals, and microbial pathogens i.e., viruses and bacteria). The comprehensive literature survey revealed that, in addition to the increased levels of pathogen and microbial threats to human wellbeing, poorly-treated wastewater results in plant and soil contamination with toxic organic/inorganic chemicals, and microbial pathogens. The impact of long-term emerging pollutants like plastic nanoparticles should also be established in further studies, with the development of standardized analytical techniques for such hazardous chemicals. Likewise, the reliable, long-term and extensive judgment on heavy metals threat to human beings's health should be explored in future investigations.

CdS nanocrystallites sensitized ZnO nanosheets for visible light induced sonophotocatalytic/photocatalytic degradation of tetracycline: From experimental results to a generalized model based on machine learning methods.

CdS/ZnO nanosheets heterostructures ((x)CdS/ZNs) with different mole ratios of Cd/Zn ((x) = 0.2, 0.4, and 0.6) were synthesized by the impregnation-calcination method. PXRD patterns showed that the (100) diffraction of ZNs was the most significant in the (x)CdS/ZNs heterostructures, and it confirmed that CdS nanoparticles (in cubic phase) occupied the (101) and (002) crystal facets of ZNs with hexagonal wurtzite crystal phase. UV-Vis DRS results indicated that CdS nanoparticles decreased the band gap energy of ZNs (2.80-2.11 eV) and extended the photoactivity of ZNs to the visible light region. The vibrations of ZNs were not observed clearly in the Raman spectra of (x)CdS/ZNs due to the extensive coverage of CdS nanoparticles shielding the deeper-laying ZNs from Raman response. The photocurrent of (0.4) CdS/ZNs photoelectrode reached 33 µA, about 82 times higher than that for ZNs (0.4 µA, 0.1 V vs Ag/AgCl). The formation of an n-n junction at the (0.4) CdS/ZNs reduced the recombination of electron-hole pairs and increased the degradation performance of the as-prepared (0.4) CdS/ZNs heterostructure. The highest percentage removal of tetracycline (TC) in the sonophotocatalytic/photocatalytic processes was obtained by (0.4) CdS/ZNs under visible light. The quenching tests showed that O2•-, h+, and OH• were the main active species in the degradation process. The degradation percentage decreased negligibly in the sonophotocatalytic (84%-79%) compared to the photocatalytic (90%-72%) process after four re-using runs due to the presence of ultrasonic waves. For the estimation of degradation behavior, two machine learning methods were applied. The comparison between the ANN and GBRT models evidenced that both models had high prediction accuracy and could be used for predicting and fitting the experimental data of the %removal of TC. The excellent sonophotocatalytic/photocatalytic performance and stability of the fabricated (x)CdS/ZNs catalysts made them promising candidates for wastewater purification.

Nanoparticles: Taking a Unique Position in Medicine.

The human nature of curiosity, wonder, and ingenuity date back to the age of humankind. In parallel with our history of civilization, interest in scientific approaches to unravel mechanisms underlying natural phenomena has been developing. Recent years have witnessed unprecedented growth in research in the area of pharmaceuticals and medicine. The optimism that nanotechnology (NT) applied to medicine and drugs is taking serious steps to bring about significant advances in diagnosing, treating, and preventing disease-a shift from fantasy to reality. The growing interest in the future medical applications of NT leads to the emergence of a new field for nanomaterials (NMs) and biomedicine. In recent years, NMs have emerged as essential game players in modern medicine, with clinical applications ranging from contrast agents in imaging to carriers for drug and gene delivery into tumors. Indeed, there are instances where nanoparticles (NPs) enable analyses and therapies that cannot be performed otherwise. However, NPs also bring unique environmental and societal challenges, particularly concerning toxicity. Thus, clinical applications of NPs should be revisited, and a deep understanding of the effects of NPs from the pathophysiologic basis of a disease may bring more sophisticated diagnostic opportunities and yield more effective therapies and preventive features. Correspondingly, this review highlights the significant contributions of NPs to modern medicine and drug delivery systems. This study also attempted to glimpse the future impact of NT in medicine and pharmaceuticals.

Intelligent route to design efficient CO2 reduction electrocatalysts using ANFIS optimized by GA and PSO.

Recently, electrochemical reduction of CO2 into value-added fuels has been noticed as a promising process to decrease CO2 emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS-PSO and ANFIS-GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO2 reduction.

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach.

Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage method that comes with pack hydrogen with high density. Among diverse methods, absorbing hydrogen on host metal is applicable at room temperature and pressure, which does not provide any safety concerns. In this regard, AB2 metal hydride with potentially high hydrogen density is selected as an appropriate host. Machine learning techniques have been applied to establish a relationship on the effect of the chemical composition of these hosts on hydrogen storage. For this purpose, a data bank of 314 data point pairs was used. In this assessment, the different A-site and B-site elements were used as the input variables, while the hydrogen absorption energy resulted in the output. A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. All the GPR models' performance was quite excellent; notably, GPR with Exponential kernel function showed the highest preciseness with R2, MRE, MSE, RMSE, and STD of 0.969, 2.291%, 3.909, 2.501, and 1.878, respectively. Additionally, the sensitivity of analysis indicated that ZR, Ti, and Cr are the most demining elements in this system.

An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique.

Tetracyclines (TCs) have been extensively used for humans and animal diseases treatment and livestock growth promotion. The consumption of such antibiotics has been ever-growing nowadays due to various bacterial infections and other pathologic conditions, resulting in more discharge into the aquatic environments. This brings threats to ecosystems and human bodies. Up to now, several attempts have been made to reduce TC amounts in the wastewater, among which photocatalysis, an advanced oxidation process, is known as an eco-friendly and efficient technology. In this regard, metal organic frameworks (MOFs) have been known as the promising materials as photocatalysts. Thus, studying TC photocatalytic degradation by MOFs would help scientists and engineers optimize the process in terms of effective parameters. Nevertheless, the costly and time-consuming experimental methods, having instrumental errors, encouraged the authors to use the computational method for a more comprehensive assessment. In doing so, a wide-ranging databank including 374 experimental data points was gathered from the literature. A powerful machine learning method of Gaussian process regression (GPR) model with four kernel functions was proposed to estimate the TC degradation in terms of MOFs features (surface area and pore volume) and operational parameters (illumination time, catalyst dosage, TC concentration, pH). The GPR models performed quite well, among which GPR-Matern model shows the most accurate performance with R2, MRE, MSE, RMSE, and STD of 0.981, 12.29, 18.03, 4.25, and 3.33, respectively. In addition, an analysis of sensitivity was carried out to assess the effect of the inputs on the TC photodegradation by MOFs. It revealed that the illumination time and the surface area play a significant role in the decomposition activity.

Dynamics of Antimicrobial Peptide Encapsulation in Carbon Nanotubes: The Role of Hydroxylation.

INTRODUCTION: Carbon nanotubes (CNTs) have been widely employed as biomolecule carriers, but there is a need for further functionalization to broaden their therapeutic application in aqueous environments. A few reports have unraveled biomolecule-CNT interactions as a measure of response of the nanocarrier to drug-encapsulation dynamics. METHODS: Herein, the dynamics of encapsulation of the antimicrobial peptide HA-FD-13 (accession code 2L24) into CNTs and hydroxylated CNTs (HCNTs) is discussed. RESULTS: The van der Waals (vdW) interaction energy of CNT-peptide and HCNT-peptide complexes decreased, reaching -110.6 and -176.8 kcal.Mol-1, respectively, once encapsulation of the peptide inside the CNTs had been completed within 15 ns. The free energy of the two systems decreased to -43.91 and -69.2 kcal.Mol-1 in the same order. DISCUSSION: The peptide was encased in the HCNTs comparatively more rapidly, due to the presence of both electrostatic and vdW interactions between the peptide and HCNTs. However, the peptide remained encapsulated throughout the vdW interaction in both systems. The negative values of the free energy of the two systems showed that the encapsulation process had occurred spontaneously. Of note, the lower free energy in the HCNT system suggested more stable peptide encapsulation.

Hyperbranched polyethylenimine functionalized silica/polysulfone nanocomposite membranes for water purification.

Hyperbranched polyethyleneimine functionalized silica (PEI-SiO2) nanoparticles with considerable hydrophilicity were synthesized and incorporated into a polysulfone (PSF)/dimethylacetamide (DMA)/polyvinylpyrrolidone (PVP) membrane casting solution in five different ratios to fabricate PEI-SiO2/PSF nanocomposite membranes using nonsolvent-induced phase separation. The hydrophilic PEI-SiO2 nanoparticles were characterized by TEM, FTIR, TGA, and XPS analyses. Morphology, water contact angles, mean pore sizes, overall porosity, tensile strengths, water flux, antifouling and the dye separation performances of the PEI-SiO2/PSF membranes were also studied. The PEI-SiO2 nanoparticles were uniformly dispersed in the PSF-based membranes, where a fall in the water contact angle was observed from 65.4° to 49.7° by addition of 2 wt% nanoparticles. The fouling resistance parameters of the PEI-SiO2/PSF membranes were declined with an increase in the nanoparticle concentration, suggesting the superior hydrophilic nature of the PEI-SiO2 nanoparticles. The permeability of the nanocomposite membranes was increased from 38.5 to 70 L m-2 h-1 bar-1 by incorporation of 2 wt% PEI-SiO2. Finally, improvements were observed in the flux recovery ratio (95.8%), Reactive Green 19 dye rejection (99.6%) and tensile strengths of the PEI-SiO2/PSF membranes over the neat PSF and SiO2/PSF membranes, which were used as controls. The results of this study demonstrate the promising application of PEI-SiO2 nanoparticles in improving the separation and antifouling performances of the PSF membranes for water purification.

An insight into thermal properties of BC3-graphene hetero-nanosheets: a molecular dynamics study.

Simulation of thermal properties of graphene hetero-nanosheets is a key step in understanding their performance in nano-electronics where thermal loads and shocks are highly likely. Herein we combine graphene and boron-carbide nanosheets (BC3N) heterogeneous structures to obtain BC3N-graphene hetero-nanosheet (BC3GrHs) as a model semiconductor with tunable properties. Poor thermal properties of such heterostructures would curb their long-term practice. BC3GrHs may be imperfect with grain boundaries comprising non-hexagonal rings, heptagons, and pentagons as topological defects. Therefore, a realistic picture of the thermal properties of BC3GrHs necessitates consideration of grain boundaries of heptagon-pentagon defect pairs. Herein thermal properties of BC3GrHs with various defects were evaluated applying molecular dynamic (MD) simulation. First, temperature profiles along BC3GrHs interface with symmetric and asymmetric pentagon-heptagon pairs at 300 K, ΔT = 40 K, and zero strain were compared. Next, the effect of temperature, strain, and temperature gradient (ΔT) on Kaptiza resistance (interfacial thermal resistance at the grain boundary) was visualized. It was found that Kapitza resistance increases upon an increase of defect density in the grain boundary. Besides, among symmetric grain boundaries, 5-7-6-6 and 5-7-5-7 defect pairs showed the lowest (2 × 10-10 m2 K W-1) and highest (4.9 × 10-10 m2 K W-1) values of Kapitza resistance, respectively. Regarding parameters affecting Kapitza resistance, increased temperature and strain caused the rise and drop in Kaptiza thermal resistance, respectively. However, lengthier nanosheets had lower Kapitza thermal resistance. Moreover, changes in temperature gradient had a negligible effect on the Kapitza resistance.

Encapsulation of an anticancer drug Isatin inside a host nano-vehicle SWCNT: a molecular dynamics simulation.

The use of carbon nanotubes as anticancer drug delivery cargo systems is a promising modality as they are able to perforate cellular membranes and transport the carried therapeutic molecules into the cellular components. Our work describes the encapsulation process of a common anticancer drug, Isatin (1H-indole-2,3-dione) as a guest molecule, in a capped single-walled carbon nanotube (SWCNT) host with chirality of (10,10). The encapsulation process was modelled, considering an aqueous solution, by a molecular dynamics (MD) simulation under a canonical NVT ensemble. The interactions between the atoms of Isatin were obtained from the DREIDING force filed. The storage capacity of the capped SWCNT host was evaluated to quantify its capacity to host multiple Isatin molecules. Our results show that the Isatin can be readily trapped inside the volume cavity of the capped SWCNT and it remained stable, as featured by a reduction in the van der Waals forces between Isatin guest and the SWCNT host (at approximately - 30 kcal mol-1) at the end of the MD simulation (15 ns). Moreover, the free energy of encapsulation was found to be - 34 kcal mol-1 suggesting that the Isatin insertion procedure into the SWCNT occurred spontaneously. As calculated, a capped SWCNT (10,10) with a length of 30 Å, was able to host eleven (11) molecules of Isatin, that all remained steadily encapsulated inside the SWCNT volume cavity, showing a potential for the use of carbon nanotubes as drug delivery cargo systems.

Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach.

In recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

Electrocatalytic hydrogen evolution on the noble metal-free MoS2/carbon nanotube heterostructure: a theoretical study.

Molybdenum disulfide (MoS2) is considered as a promising noble-metal-free electrocatalyst for the Hydrogen Evolution Reaction (HER). However, to effectively employ such material in the HER process, the corresponding electrocatalytic activity should be comparable or even higher than that of Pt-based materials. Thus, efforts in structural design of MoS2 electrocatalyst should be taken to enhance the respective physico-chemical properties, particularly, the electronic properties. Indeed, no report has yet appeared about the possibility of an HER electrocatalytic association between the MoS2 and carbon nanotubes (CNT). Hence, this paper investigates the synergistic electrocatalytic activity of MoS2/ CNT heterostructure for HER by Density Functional Theory simulations. The characteristics of the heterostructure, including density of states, binding energies, charge transfer, bandgap structure and minimum-energy path for the HER process were discussed. It was found that regardless of its configuration, CNT is bound to MoS2 with an atomic interlayer gap of 3.37 Å and binding energy of 0.467 eV per carbon atom, suggesting a weak interaction between CNT and MoS2. In addition, the energy barrier of HER process was calculated lower in MoS2/CNT, 0.024 eV, than in the MoS2 monolayer, 0.067 eV. Thus, the study elaborately predicts that the proposed heterostructure improves the intrinsic electrocatalytic activity of MoS2.

Atomic simulation of adsorption of SO2 pollutant by metal (Zn, Be)-oxide and Ni-decorated graphene: a first-principles study.

Due to the impact of toxic gases on human health, considerable interest has been shown in detecting noxious air pollutants, particularly sulfur dioxide (SO2), both experimentally and theoretically. This work provides new insights into the adsorbing (SO2) molecules on the surface of metal-oxide graphitic structures, i.e., Beryllium-Oxide (BeO), Zinc-Oxide (ZnO), and Ni-decorated graphene applying a first-principles study. Computational analyses suggest that the type of binding of SO2 molecule on BeO and ZnO sheets is physisorption so that binding energies of -0.405 and -0.154 eV were assigned to ZnO and BeO nanosheets in that order. The adsorption energy of SO2 on metal oxide sheets was much higher than the pristine graphene. Taking pristine graphene as an adsorbent for SO2 molecule, it was found that such nanomaterial is not an efficient adsorbent due to the weak interactions (-0.157 eV) and low electron charge transfer (0.042 e) present in SO2/graphene complex. To overcome this issue, graphene nanosheets decorated with nickel atoms were studied for interaction with SO2 molecules; the results indicate that the SO2 molecules were chemisorbed on Ni-decorated graphene sheets with an adsorption energy of -2.297 eV. Chemisorption of SO2 molecules on Ni-decorated graphene sheets was proven by the strong orbital hybridization between Ni 3d and sulfur 3p orbitals in the Projected Density of States (PDOS) plot. This work provides useful information about SO2 adsorption on Ni-decorated graphene sheets in order to develop a new class of gas sensing devices. Superior chemisorption of SO2 on Ni-decorated graphene sheets compared to the physical adsorption on BeO and ZnO sheets makes Ni-decorated graphene a potential candidate for detecting SO2 molecules.

Kinetics of Cross-Linking Reaction of Epoxy Resin with Hydroxyapatite-Functionalized Layered Double Hydroxides.

The cure kinetics analysis of thermoset polymer composites gives useful information about their properties. In this work, two types of layered double hydroxide (LDH) consisting of Mg2+ and Zn2+ as divalent metal ions and CO32- as an anion intercalating agent were synthesized and functionalized with hydroxyapatite (HA) to make a potential thermal resistant nanocomposite. The curing potential of the synthesized nanoplatelets in the epoxy resin was then studied, both qualitatively and quantitatively, in terms of the Cure Index as well as using isoconversional methods, working on the basis of nonisothermal differential scanning calorimetry (DSC) data. Fourier transform infrared spectroscopy (FTIR) was used along with X-ray diffraction (XRD) and thermogravimetric analysis (TGA) to characterize the obtained LDH structures. The FTIR band at 3542 cm-1 corresponded to the O-H stretching vibration of the interlayer water molecules, while the weak band observed at 1640 cm-1 was attributed to the bending vibration of the H-O of the interlayer water. The characteristic band of carbonated hydroxyapatite was observed at 1456 cm-1. In the XRD patterns, the well-defined (00l) reflections, i.e., (003), (006), and (110), supported LDH basal reflections. Nanocomposites prepared at 0.1 wt % were examined for curing potential by the Cure Index as a qualitative criterion that elucidated a Poor cure state for epoxy/LDH nanocomposites. Moreover, the curing kinetics parameters including the activation energy (Eα), reaction order, and the frequency factor were computed using the Friedman and Kissinger-Akahira-Sunose (KAS) isoconversional methods. The evolution of Eα confirmed the inhibitory role of the LDH in the crosslinking reactions. The average value of Eα for the neat epoxy was 54.37 kJ/mol based on the KAS method, whereas the average values were 59.94 and 59.05 kJ/mol for the epoxy containing Zn-Al-CO3-HA and Mg Zn-Al-CO3-HA, respectively. Overall, it was concluded that the developed LDH structures hindered the epoxy curing reactions.

Effect of Surface Treatment of Halloysite Nanotubes (HNTs) on the Kinetics of Epoxy Resin Cure with Amines.

The epoxy/clay nanocomposites have been extensively considered over years because of their low cost and excellent performance. Halloysite nanotubes (HNTs) are unique 1D natural nanofillers with a hollow tubular shape and high aspect ratio. To tackle poor dispersion of the pristine halloysite (P-HNT) in the epoxy matrix, alkali surface-treated HNT (A-HNT) and epoxy silane functionalized HNT (F-HNT) were developed and cured with epoxy resin. Nonisothermal differential scanning calorimetry (DSC) analyses were performed on epoxy nanocomposites containing 0.1 wt.% of P-HNT, A-HNT, and F-HNT. Quantitative analysis of the cure kinetics of epoxy/amine system made by isoconversional Kissinger-Akahira-Sunose (KAS) and Friedman methods made possible calculation of the activation energy (Eα) as a function of conversion (α). The activation energy gradually increased by increasing α due to the diffusion-control mechanism. However, the average value of Eα for nanocomposites was lower comparably, suggesting autocatalytic curing mechanism. Detailed assessment revealed that autocatalytic reaction degree, m increased at low heating rate from 0.107 for neat epoxy/amine system to 0.908 and 0.24 for epoxy/P-HNT and epoxy/A-HNT nanocomposites, respectively, whereas epoxy/F-HNT system had m value of 0.072 as a signature of dominance of non-catalytic reactions. At high heating rates, a similar behavior but not that significant was observed due to the accelerated gelation in the system. In fact, by the introduction of nanotubes the mobility of curing moieties decreased resulting in some deviation of experimental cure rate values from the predicted values obtained using KAS and Friedman methods.