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Abstract Hydrogen evolution reaction (HER) stands out among conventional hydrogen production processes by featuring excellent advantages. However, the uncompetitive production cost due to the low energy efficiency has hindered its development, necessitating the introduction of cost-effective electrocatalysts. In this study, we introduced samarium doping as a high-potential approach to improve the electrocatalytic properties of nickel phosphide (Ni2P) for efficient HER. Samarium-doped Ni2P was synthesized via a facile two-step vapor–solid reaction technique. Different physical and electrochemical analyses showed that samarium doping significantly improved pure Ni2P characteristics, such as particle size, specific surface area, electrochemical hydrogen adsorption, intrinsic activity, electrochemical active surface area, and charge transfer ability in favor of HER. Namely, Ni2P doped with 3%mol of samarium (Sm0.03Ni2P) with a Tafel slope of 67.8 mV/dec. and overpotential of 130.6 mV at a current density of 10 mA/cm2 in 1.0 M KOH solution exhibited a notable performance, suggesting Sm0.03Ni2P and samarium doping as a remarkable electrocatalyst and promising promoter for efficient HER process, respectively.

Abstract Introduction Pedestrians are considered the most vulnerable and complex road users as human behavior constitutes one of the fundamental reasons for traffic-related incidents involving pedestrians. However, the role of health literacy as a predictor of Pedestrian safety behavior remains underexplored. Therefore, the current study was designed to examine the level of health literacy and its association with the safety behavior of adult pedestrians in the city of Tabriz. Methods This cross-sectional analytical study was conducted among individuals aged 18 to 65 years in the metropolitan area of Tabriz from January to April 2023. Data were collected using the HELIA standard questionnaire (Health Literacy Instrument for adults), comprising 33 items across 5 domains (access, reading, understanding, appraisal, decision-making and behavior), as well as the Pedestrian Behavior Questionnaire (PBQ) consisting of 29 items. Data were analyzed using descriptive and analytical statistics (independent t-tests, ANOVA, and Pearson correlation coefficient) via SPSS-22 software. Results Based on the results, 94% (376 individuals) had excellent health literacy levels, and their safety behavior scores were at a good level. Health literacy and safety behavior were higher among the age group of 31 to 45 years, women, married individuals, those who read books, and individuals with higher education. However, safety behavior showed no significant association with education level (P > 0.05). There was a significant and positive relationship between health literacy and all its domains and pedestrian safety behavior (r = 0.369, P

Abstract 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.

Sustainability and lack of resources both outline need for energy storage tactics, materials, and devices. In fact, energy storage is nowadays is the most important, at the same time challenging feature in under development and developing countries. Renewable energies are focused as minimizing energy consumption, whereas maximizing storage of energies. Geopolitical features of energy storage together with wars in the middle-east and Europe borderline criticize such important topic more and more. Rechargeable batteries have widely been served and developed continuously in electronic devices as a means of storing electrical energy. Therefore, increasing the capacity and life of batteries has become a target for researchers working in this field. Enlargement of application and performance windows of batteries have become possible by the era of nanotechnology, such that new generations of rechargeable batteries enjoy from much higher efficiency and performance with respect to first-generation ones. In this article, after examining the electrochemical preparations, batteries and their types are discussed. Alkaline and lead-acid batteries and their application are also discussed on the basis of nanotechnology.

Nanoparticles have revolutioned materials secience and manufacturing industry thanks to their ultrafine scale. Nano-scale titanium oxide (nano-TiO2) are key elements of a wide variety of sectors of advanced materials, devices, and systems in view of their versatility and extraordinary characetristics, mainly optical, electrical and catalytic properties. Thnaks to aforementioned features, they found a key position in different industries such as colors and pigments, photocatalysts, photovoltaic solar cells, adsorbents, cosmetics, electric and electronic devices. Nano-SiO2 application gives rise to a wide range of properties and performance, which severely depends on particle size, particle size distribution, and agglomeration in the bulk of host materials or systems. When they are used as surface coating additive or even functionalized, nano- TiO2 takes more roles, which are state-of-the-art depending on synthesis method. Therefore, review of synthesis, properties, surface- and bulk-functionalization/modification (respectively with function groups and doping) methods in achieving high-performance nano- TiO2 seems essential. In this review, background information and developments from pure Titanium Oxide to chemically modified Titanium Oxide -based materials as photocatalysts were discussed in detail, Photocatalytic activity of Titanium oxide-based materials in nitrogen group pollutant decomposition. It also Titanium Oxide Nanoparticles as a Vehicle for Chemotherapeutics and Other Applications of Titanium Oxide Nanoparticles in Medicine and also an overview of the synthesis methods and factors affecting their properties in different synthesis methods have been reviewed.

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.

Abstract One of the present great challenges is finding nonprecious materials characterized by efficient electrocatalytic behavior in order to substitute the expensive platinum-based materials for the purpose of hydrogen evolution reactions (HERs). In this study, ZIF-67 and ZIF-67 were used as precursors in order to fabricate metallic-doped N-enriched carbon successfully through a simple process of pyrolysis for applying the hydrogen evolution reaction. In addition, nickel was added to these structures in the course of the synthesis procedure. While under high-temperature treatment, Nickel doped ZIF-67 was transformed into metallic NiCo doped N enriched carbon (NiCo/NC), under high-temperature treatments, Ni-doped ZIF-8 changed into metallic NiZn doped N enriched carbon (NiZn/NC). By combining metallic precursors, the following five structures were synthesized: NiCo/NC, Co/NC, NiZn/NC, NiCoZn/NC, as well as CoZn/NC. It is noteworthy that the produced Co/NC shows optimum hydrogen evolution reaction activity along with superior overpotential of 97 mV and the minimum Tafel slope of 60 mV/dec at 10 mA cm. In addition, the superb behavior of hydrogen evolution reaction can be attributable to the numerous active sites, the superior electrical conductivity of carbon, and the firm structure. As a result, the present paper suggests a novel strategy in order to produce nonprecious materials characterized by superb HER efficiency for future scholars.

Abstract 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.

Abstract 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.

Abstract Pyrolysis gasoline is the valuable byproduct of the thermal breakdown of heavier oil fractions in an olefin unit with high aromatic content. To separate such aromatic components, firstly, this product should be hydrogenated. In this contribution, new nanostructure catalysts derived from the zeolitic metal–organic framework, namely ZIF-8 and ZIF-67, were used to investigate their hydrogenation capability. Owing to its great hydrogenation capability of Nickle, the structures of the ZIF-8 and ZIF-67 were improved by Nickle through in situ synthesis. Moreover, to enhance the pore size of catalysts and their electronic properties, the synthesized catalysts were pyrolyzed under nitrogen media at 450 °C, and five catalysts, namely Co/NC, ZnCo/NC, ZnNi/NC, CoNi/NC, and ZnCoNi/NC were created. Results indicated that the CoNi/NC showed a superior hydrogenation performance (69.5% conversion of total olefins) to others. In addition, the synthesized catalysts without the carbonization process had no conversion in the hydrogenation process because there is no active site in these structures. The current synthesized catalysts can compete with the costly Pt or Pd-based hydrogenation catalysts due to their high surface area and great electronic properties.

Abstract Several studies have been directed to find scalable, swift, accurate, and cost‐effective strategies for detecting, monitoring, and treating coronavirus disease 2019 (COVID‐19). Indeed, the lack of a fast and practical method for detecting the infected regions makes decision‐making challenging to combat the critical pandemic‐struck situations. The probable ‘wrong’, or rather inadequate, decisions not only have a boomerang effect on the economy but also can lead to an increase in the number of infected individuals, degree of hospitalization, and death counts. Although the current clinical methods are effective, they are costly, time‐consuming, and, more particularly, inadequate because of the virus's mutation patterns. In addition, contamination of biomedical wastes with the COVID‐19 virus is a matter of grave concern. Therefore, there is a perpetual need for novel methodologies to delineate the contaminated regions and determine whether those viruses contaminate the wastewater. Although several review papers have been recently published to discuss those concerns, there is a lack of a comprehensive survey of the detection and treatment of the COVID‐19 virus in aqueous media. Herein, we review techniques available as spreading signifiers for detecting the COVID‐19 virus in water resources and wastewater. We classify and integrate techniques into wastewater, sewage, and sludge detection and monitoring. Treatment of COVID‐19‐contaminated wastewater is discussed by classifying and ranking the methodologies nurtured from nanotechnology, including nanoparticle‐based biosensors used in the detection and nanotechnology‐based filtration systems for the removal of COVID‐19 from wastewater. We also highlight the compilation of the detection methodologies in contaminated aqueous media and provide insight into the challenges associated with treating COVID‐19‐contaminated wastewater. The article concludes that international and robust guidelines for virus/bacteria treatment in wastewater are urgently needed to protect the environment and public health, where nanotechnology plays a key role.

Abstract Optimization of thermal conductivity of nanomaterials enables the fabrication of tailor-made nanodevices for thermoelectric applications. Superlattice nanostructures are correspondingly introduced to minimize the thermal conductivity of nanomaterials. Herein we computationally estimate the effect of total length and superlattice period ( $$l_{p}$$ l p ) on the thermal conductivity of graphene/graphane superlattice nanoribbons using molecular dynamics simulation. The intrinsic thermal conductivity ( $$\kappa_{\infty }$$ κ ∞ ) is demonstrated to be dependent on $$l_{p}$$ l p . The $$\kappa_{\infty }$$ κ ∞ of the superlattice, nanoribbons decreased by approximately 96% and 88% compared to that of pristine graphene and graphane, respectively. By modifying the overall length of the developed structure, we identified the ballistic-diffusive transition regime at 120 nm. Further study of the superlattice periods yielded a minimal thermal conductivity value of 144 W m−1 k−1 at $$l_{p}$$ l p = 3.4 nm. This superlattice characteristic is connected to the phonon coherent length, specifically, the length of the turning point at which the wave-like behavior of phonons starts to dominate the particle-like behavior. Our results highlight a roadmap for thermal conductivity value control via appropriate adjustments of the superlattice period.

Abstract 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.

Abstract Single-atom catalysts (SACs) introduce as a promising category of electrocatalysts, especially in the water-splitting process. Recent studies have exhibited that nitrogen-doped carbon-based SACs can act as a great HER electrocatalyst. In this regard, Adaptive Neuro-Fuzzy Inference optimized by Gray Wolf Optimization (GWO) method was used to predict hydrogen adsorption energy (ΔG) obtained from density functional theory (DFT) for single transition-metal atoms including Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, Hf, Ta, W, Re, Os, Ir, Pt, and Au embedded in N-doped carbon of different sizes. Various descriptors such as the covalent radius, Zunger radius of the atomic d-orbital, the formation energy of the single-atom site, ionization energy, electronegativity, the d-band center from − 6 to 6 eV, number of valence electrons, Bader charge, number of occupied d states from 0 to − 2 eV, and number of unoccupied d states from 0 to 2 eV were chosen as input parameters based on sensitivity analysis. The R-squared and MSE of the developed model were 0.967 and 0.029, respectively, confirming its great accuracy in determining hydrogen adsorption energy of metal/NC electrocatalysts.

Abstract 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.

Early diagnosis is essential for the effective illness treatment, but traditional diagnostic approaches inevitably have major downsides. Recent advancements in nanoparticle-based biosensors have created new opportunities for accelerating diagnosis. High surface area, exceptional sensitivity, high specificity, and optical characteristics of metal and metal oxide nanoparticles have made it possible to detect a variety of health conditions and diseases immediately, including cancer, viral infection, biomarkers, and in-vivo imaging. Metal nanoparticles may be produced in a variety of ways, enabling the creation of innovative tools for chemical and biological sensing targets. The utilization of various metal nano-formulations, metal oxide nanoplatforms, and their composites in the early identification of illnesses is reported and summarized in this review. Additionally, the challenging corners in the use of metal oxide-based nano-scale diagnostic technologies in clinical applications are highlighted. The current work is believed to serve as a roadmap for in-depth research on inorganic nanomedicine, both in-vitro and in-vivo diagnosis of diseases and illnesses, especially pandemic infections like COVID-19.

Abstract 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.

Abstract 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.

Abstract 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.

Introduction: Chemotherapy with anti-cancer drugs is considered the most common approach for killing cancer cells in the human body. However, some barriers such as toxicity and side effects would limit its usage. In this regard, nano-based drug delivery systems have emerged as cost-effective and efficient for sustained and targeted drug delivery. Nanotubes such as carbon nanotubes (CNT) and boron nitride nanotubes (BNNT) are promising nanocarriers that provide the cargo with a large inner volume for encapsulation. However, understanding the insertion process of the anti-cancer drugs into the nanotubes and demonstrating drug-nanotube interactions starts with theoretical analysis. Methods: First, interactions parameters of the atoms of 5-FU were quantified from the DREIDING force field. Second, the storage capacity of BNNT (8,8) was simulated to count the number of drugs 5-FU encapsulated inside the cavity of the nanotubes. In terms of the encapsulation process of the one drug 5-FU into nanotubes, it was clarified that the drug 5-FU was more rapidly adsorbed into the cavity of the BNNT compared with the CNT due to the higher van der Waals (vdW) interaction energy between the drug and the BNNT. Results: The obtained values of free energy confirmed that the encapsulation process of the drug inside the CNT and BNNT occurred spontaneously with the free energies of −14 and −25 kcal·mol−1, respectively. Discussion: However, the lower value of the free energy in the system containing the BNNT unraveled more stability of the encapsulated drug inside the cavity of the BNNT comparing the system having CNT. The encapsulation of Fluorouracil (5-FU) anti-cancer chemotherapy drug (commercial name: Adrucil®) into CNT (8,8) and BNNT (8,8) with the length of 20 Å in an aqueous solution was discussed herein applying molecular dynamics (MD) simulation.

In this present study, various types of TiO2 nanostructures were synthesised via hydrothermal method from a commercial titanium dioxide. The effects of the initial concentration of titanium dioxide and the reaction time on the morphology of synthesised nanostructures were investigated. The TiO2 nanostructures were calcined at 500 °C and examined for the photocatalytic performance by decomposing formic acid as an organic pollutant. Scanning electron microscopy, transmission electron microscopy, Brunauer–Emmett–Teller and X-ray diffraction were employed to characterise the synthesised TiO2 nanostructures. The outcomes showed more influence of reaction time rather than initial TiO2 concentration on the properties of TiO2 nanostructures. Various TiO2 nanostructures such as, nanorods and nanotubes were fabricated at different initial TiO2 concentrations and reaction times. In addition, the synthesised nanorod structures showed higher photocatalytic activity than the nanotubes. This is owing to the presence of rutile–anatase combined crystalline phases in the nanorod structures.

The efficient removal of pollutants from different environments has been one of the great challenges for scientists in recent years. However, the understanding of the mechanisms underlying this phenomenon is still the subject of passionate debates, mainly due to the lack of experimental tools capable of detecting events at the atomic scale. Herein, a comparative theoretical study was carried out to capture the adsorption of H2S on metal oxide surfaces such as zinc oxide (ZnO) and beryllium oxide (BeO), as well as graphene and Ni-decorated graphene. A simulation based on density-functional theory (DFT) was carried out by adopting General Gradient Approximation (GGA) under the Perdew–Burke–Ernzerhof (PBE) function. The calculations quantified H2S adsorption on the considered metal oxide sheets as well as on the non-decorated graphene having a physical nature. In contrast, H2S adsorbed on Ni-decorated graphene sheet gave an adsorption energy of −1.64 eV due to the interaction of S and Ni atoms through the formation of a covalent bond, proof of chemisorption. It seems that the graphene sheet decorated with Ni atoms is a more suitable adsorbent for H2S molecules than BeO, ZnO, or non-decorated graphene, providing a theoretical basis for future studies.