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.

Design of 2D/2D β-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, β- Ni(OH)2 nanosheets were synthesized through visible-light-induced photodeposition of different amounts of nickel on ZnO nanosheets (β-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 β-Ni(OH)2. The FESEM and TEM micrographs showed that the β-Ni(OH)2 sheets were dispersed on the surface of ZNs and formed 2D/2D p-n heterojunction in β-Ni(x)/ZNs samples. With the photodeposition of β-Ni(OH)2 nanosheets on ZNs, the surface area, pore volume, and pore diameter of β-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 β-Ni(0.1)/ZNs and β-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 (β-Ni(x)/ZNs) is more sustainable than the conventional ZnO photocatalytic systems in practical applications.

Introducing a new exchange functional by altering the electron density's ionization dependency in density functional theory

Each of the exchange-correlation functionals in the density functional theory has been customized to particular systems or elements and has unique advantages and disadvantages. In one of the most recent research on exchange-correlation functionals, Chachiyo et al. present a relationship for exchange-correlation functional by assuming the simplest form of electron density. Their utilized electron density causes a systematic inaccuracy in the energy of the molecules since it does not fully account for the variation of the ionization energy for different atoms. We offer a novel relationship for exchange functional that improves the precision of the energy calculations for molecules by using the basic assumptions of the Chachiyo approach and correcting the electron density. Our density is directly related to the atom's ionization energy. Our suggested functional was implemented for 56 molecules composed of atoms from the first, second, and third rows of the periodic table using Siam Quantum package. We discussed about the role of our functional on the reducing the computation error of dipole moment along with total, bonding and zero point energies. We also increased the portion of core electrons to improve the accuracy of the results.

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.

Polysaccharide nanocomposites in wastewater treatment: A review

In modern times, wastewater treatment is vital due to increased water contamination arising from pollutants such as nutrients, pathogens, heavy metals, and pharmaceutical residues. Polysaccharides (PSAs) are natural, renewable, and non-toxic biopolymers used in wastewater treatment in the field of gas separation, liquid filtration, adsorption processes, pervaporation, and proton exchange membranes. Since addition of nanoparticles to PSAs improves their sustainability and strength, nanocomposite PSAs has gained significant attention for wastewater treatment in the past decade. This review presents a comprehensive analysis of PSA-based nanocomposites used for efficient wastewater treatment, focusing on adsorption, photocatalysis, and membrane-based methods. It also discusses potential future applications, challenges, and opportunities in adsorption, filtration, and photocatalysis. Recently, PSAs have shown promise as adsorbents in biological-based systems, effectively removing heavy metals that could hinder microbial activity. Cellulose-mediated adsorbents have successfully removed various pollutants from wastewater, including heavy metals, dyes, oil, organic solvents, pesticides, and pharmaceutical residues. Thus, PSA nanocomposites would support biological processes in wastewater treatment plants. A major concern is the discharge of antibiotic wastes from pharmaceutical industries, posing significant environmental and health risks. PSA-mediated bio-adsorbents, like clay polymeric nanocomposite hydrogel beads, efficiently remove antibiotics from wastewater, ensuring water quality and ecosystem balance. The successful use of PSA-mediated bio-adsorbents in wastewater treatment depends on ongoing research to optimize their application and evaluate their potential environmental impacts. Implementing these eco-friendly adsorbents on a large scale holds great promise in significantly reducing water pollution, safeguarding ecosystems, and protecting human health.

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.

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.

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.

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.

Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches

Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. Several operational parameters can affect the efficiency of photocatalytic degradation. Performing experimental methods to obtain the percentage degradation (%degradation) of pollutants in different operating conditions is costly and time-consuming. For this reason, the use of computational models is very useful to present the %degradation in various operating conditions. In our previous work, Fe3O4/TiO2 nanocomposite containing different amounts of silver nanoparticles (Fe3O4/TiO2/Ag) were synthesized, characterized by various analytical techniques and applied to degradation of 2,4-dichlorophenol (2,4-DCP). In this work, a series of models, including stochastic gradient boosting (SGB), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), the improvement of ANFIS with genetic algorithm (GA-ANFIS), and particle swarm optimization (PSO-ANFIS) were developed to estimate the removal percentage of 2,4-DCP. The model inputs comprised of catalyst dosage, radiation time, initial concentration of 2,4-DCP, and various volumes of AgNO3. Evaluating the developed models showed that all models can predict the occurring phenomena with good compatibility, but the PSO-ANFIS and the SGB models gave a high accuracy with the coefficient of determination (R 2) of 0.99. Moreover, the relative contributions, and the relevancy factors of input parameters were evaluated. The catalyst dosage and radiation time had the highest (32.6%), and the lowest (16%) relative contributions on the predicting of removal percentage of 2,4-DCP, respectively.

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.

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.

Comparison of efficiency of engineering and administrative interventions on risk level of occupational hazards in task of emergency nurses in yazd Shahid Rahnamoon hospital

Introduction: Emergency ward nurses are exposed to occupational hazards. Job Safety Analysis (JSA) is a way to identify and assess job-related risks and provide control strategies to reduce risks. The purpose of this study was to evaluate Job Safety Analysis and compare the efficacy of control (engineering and administrative) interventions in emergency nursing. Materials and Methods: This was an interventional study performed to assess the risk by Job Safety Analysis (JSA) in three groups of nurses working in emergrncy ward of Shahid Rahnemoon hospital in 2019. First, the initial risk assessment code (pre-intervention) estimated, then the engineering and administrative controls were implemented and the secondary risk assessment code (after intervention) was calculated after three months. Results: According to the results of the study، risk of musculoskeletal disorders with risk score of 20, was identified as the highest risk in all three emergency nursing groups, also mean risk assessment code for the occupational hazards in nurses was calculated which was in the unacceptable risk range and reached an acceptable level after performing administrative and engineering interventions. Conclusion: Results of this study showed that the implementation of engineering and administrative interventions had a positive effect on reducing the mean risk assessment code, also risk assessment code for occupational hazards reduced to almost the same amount with the implementation of each engineering and administrative intervention. This indicates similar role for these interventions in reducing the risk level.

Ascariasis in common bile duct resulting in a subhepatic abscess

BACKGROUND

Biliary system ascariasis can be a rare cause of acute abdomen.

PATIENT REPORT

A 70-year-old woman presented with abdominal pain for two weeks. She complained of a right upper quadrant (RUQ) pain, intermittent vomiting and weight loss. Physical examination showed RUQ and epigastric tenderness without rebound tenderness or guarding. Laboratory finding exhibited leukocytosis and mildly elevated liver enzymes.

RESULTS

Abdominal sonography showed distended gallbladder and a tubular lesion inside the common bile duct (CBD) in favor of a parasitic lesion. A large ascaris roundworm and blood clots were extracted from the CBD by endoscopic retrograde cholangiopancreatography and sphincterotomy. After 7 days, the disease process was complicated with a subhepatic abscess formation which was managed conservatively. Finally, the patient was discharged home in fair condition after 12 days of hospitalization.

CONCLUSIONS

Ascariasis should be considered in endemic areas and early endoscopic intervention and medical therapy can be effective for extrahepatic involvement.

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 (E_α) 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 E_α 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.

P3701Short-term decrease of left atrial size predicts clinical outcome in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

Background

Increased left atrium (LA) size is a hallmark of severe aortic stenosis (AS) and is associated with adverse patients' cardiovascular outcome. Whether transcatheter aortic valve replacement (TAVR) may lead to a decrease in LA size is not known. Aim: We investigated whether TAVR results in a short-term decrease in LA size and whether such decrease may predict patients' clinical outcome.

Methods

104 consecutive patients with severe symptomatic AS and dilated LA undergoing TAVR were enrolled. LA volume was assessed by echocardiography before and shortly after TAVR (median time: 7 days). Composite rate of death and hospitalization for acutely decompensated heart failure (HF) was recorded and clinical status was assessed through NYHA- class evaluation at 12 months median follow-up.

Results

After TAVR, 49 patients (47%) demonstrated a decrease in LA volume. Despite a similar baseline NYHA class, patients with decrease in LA size had significant better improvement in clinical status respect to patients with unvaried LA size (NYHA post: 1.2±0.6 vs 1.8±1.1, p=0.001; NYHA reduction: −1.6±0.9 vs −0.9±1.0, p=0.002, respectively). Moreover, these patients had a significantly reduced rate of death or HF-hospitalization (4 vs 29%, p=0.001) and a significantly longer event-free-survival from Kaplan-Meier curves (p=0.003). COX regression analysis showed that, among echocardiographic parameters, decrease in LA-size was an independent predictor of clinical outcome (HR: 0.149, CI: 0.034–0.654, p=0.012).

Conclusions

The lack of decrease in LA size shortly after TAVR is associated with significantly higher rates of death and HF-hospitalization, as well as with impaired improvement in clinical status during long-term follow-up.

Assessment of drowning status based on age groups in the population covered by Isfahan ‎University of Medical Sciences from 2012 to 2017‎

Background:

According to reports from the World Health Organization, drowning is the third cause of death ‎due to unintentional accidents and accidents, which is high among children under the age of 5 ‎years. In addition, a large number of children who survive the risk of drowning endure long-‎term outcomes and disabilities, which impose a significant spiritual and material burden on ‎families and the health system of countries. Over the past year, according to a report from the ‎Forensic Medicine Organization, 1,269 people died of drowning, of which 858 were male and ‎‎168 were women. In this regard, information collected from the one-to-59-month-old children's ‎care system in 1396 shows that 12 percent of deaths from unintentional accidents in this age ‎group are drowning. The review of texts and articles suggests that the implementation of ‎existing laws or regulations, or the formulation of new regulations on the optimization of the ‎environment in pools, wells and fish farming ponds, and enhancing safety within the seaside, ‎lakes and rivers' seabeds, on education of the people , Will be very effective. ‎

Methods:

A cross-sectional study was carried out using data recording software and death records. Data ‎was analyzed using SPSS software.

Results:

Based on the information available in the accident registration software, during the years 2012 ‎to 2017, 606799 people were referred to hospitals affiliated to Isfahan University of Medical ‎Sciences due to accidents, of which 190 were drowned, of which 63 (33.2%) have died. Also, ‎according to this information, the most common occurrence is drowning in the age group of 18 ‎to 29 years, which is 29.47% of all deaths due to drowning in 2012-2017. The second rate of ‎death from drowning is 27.3% for the age group of 30 to 59 years, and the next rank with the ‎percentage of 26.31 for the age group of 6 to 18 years and 13.15% for the age group under 5 ‎years.‎

Conclusions:

The findings of this study suggest that the following measures can be taken to prevent and ‎reduce drowning events: Holding the Supreme Committee of the Safe Community of the City ‎and the above plans, a joint meeting with program-related organizations 3. Conduct training ‎sessions to prevent drowning in the public, Supervise related sites (swimming pools, etc.) ‎regarding the supply of equipment and emergency shelter, Co-ordination with municipalities for ‎the repair and safety of ponds and ponds in parks and other public places, First aid training ‎through holding training and distributing pamphlets and tracks, Notification via audio and ‎video, Co-ordination with agriculture Jihad for the safety of canals and agricultural land ponds, ‎Co-ordination with the Water and Wastewater Authority and the municipalities to secure the ‎wells and Installing signs and warning signs along rivers and canals and Immunization of baths ‎Pools and ponds in the home.‎

Keywords:

Drowning-age groups, Software, Recording of accidents, Death record software

Environmental impact reduction as a new dimension for quality measurement of healthcare services

PURPOSE

The purpose of this paper is to provide a detailed accounting of energy and materials consumed during magnetic resonance imaging (MRI).

DESIGN/METHODOLOGY/APPROACH

The first and second stages of ISO standard (ISO 14040:2006 and ISO 14044:2006) were followed to develop life cycle inventory (LCI). The LCI data collection took the form of observations, time studies, real-time metered power consumption, review of imaging department scheduling records and review of technical manuals and literature.

FINDINGS

The carbon footprint of the entire MRI service on a per-patient basis was measured at 22.4 kg CO₂eq. The in-hospital energy use (process energy) for performing MRI is 29 kWh per patient for the MRI machine, ancillary devices and light fixtures, while the out-of-hospital energy consumption is approximately 260 percent greater than the process energy, measured at 75 kWh per patient related to fuel for generation and transmission of electricity for the hospital, plus energy to manufacture disposable, consumable and reusable products. The actual MRI and standby energy that produces the MRI images is only about 38 percent of the total life cycle energy.

RESEARCH LIMITATIONS/IMPLICATIONS

The focus on methods and proof-of-concept meant that only one facility and one type of imaging device technology were used to reach the conclusions. Based on the similar studies related to other imaging devices, the provided transparent data can be generalized to other healthcare facilities with few adjustments to utilization ratios, the share of the exam types, and the standby power of the facilities' imaging devices.

PRACTICAL IMPLICATIONS

The transparent detailed life cycle approach allows the data from this study to be used by healthcare administrators to explore the hidden public health impact of the radiology department and to set goals for carbon footprint reductions of healthcare organizations by focusing on alternative imaging modalities. Moreover, the presented approach in quantifying healthcare services' environmental impact can be replicated to provide measurable data on departmental quality improvement initiatives and to be used in hospitals' quality management systems.

ORIGINALITY/VALUE

No other research has been published on the life cycle assessment of MRI. The share of outside hospital indirect environmental impact of MRI services is a previously undocumented impact of the physician's order for an internal image.

New functions and graphical user interface attached to powder indexing software CONOGRAPH

The present status of powder indexing software CONOGRAPH as an integrated system with a graphical user interface is reported. The improvements to the peak search method and the other available functions are introduced. Computational results and times for CONOGRAPH are compared with those for ITO13, N-TREOR and DICVOL14 to clarify their differences.

Blue-white screening as a new readout for deoxyribozyme activity in bacterial cells

Demonstration of 10–23 deoxyribozyme activity in viable E. coli using blue-white screening as the readout system.

Simulation of the Selective Hydrogenation of C3-Cut in the Liquid Phase

In this work, the selective hydrogenation of C3-cut in the liquid phase has been investigated. A realistic model has been developed to simulate the selective transformation of methylacetylene (MA) and propadiene (PD) into propylene in an industrial-scale reactor. The reaction rates have been determined for the gas phase hydrogenation process and adapted for the liquid phase by considering several changes including the catalyst activity and the concentration of reactive components. In the last part of this work, a three-phase hydrodynamic model was developed and the kinetic and hydrodynamic models were then combined in order to achieve the global reactor model. In addition, the results obtained from the model were compared to those from an industrial reactor. It was found that there is a good agreement between the simulation predictions and the output of the real industrial reactor.

Oral delivery of nanoparticles containing anticancer SN38 and hSET1 antisense for dual therapy of colon cancer

An oral delivery system intended for treatment of colon cancer in HT29 cancerous cells was investigated by encapsulating hSET1 antisense and SN38 anticancer in nanoparticles based on cysteine trimethyl chitosan (cysTMC) and carboxymethyl dextran (CMD). Studies have shown hSET1 as the main type of histone methyltransferase (HMT) complex, is significantly overexpressed in malignant cells. In this study, hSET1 antisense was employed to inhibit gene expression. Additionally, SN38 was incorporated into nanoparticles to enhance the efficiency of the system by inhibition of topoisomerase 1. CysTMC was synthetized and characterized by (1)H NMR and FTIR. Nanoparticles were prepared through complexation of CMD and cysTMC. Particle size and surface charge was 100-150 nm and 17-21 mV respectively with drug content of around 2.6%. Gel electrophoresis assay proved the stability of antisense in simulated gastric and intestinal fluids. Nanoparticles showed high mucoadhesion and glutathione responsive release. Cellular uptake was observed by confocal microscopy and quantified by flow cytometry. Cytotoxicity of NPs was assessed using MTT assay. Results showed hSET1/SN38 nanoparticles had significantly higher cytotoxicity against HT29 cells compared with nanoparticles containing SN38, free SN38 or naked hSET1. Therefore, present system could be considered an effective combination therapy of highly hydrophobic SN38 and hSET1.

Cubic Ag/AgBr–graphene oxide nanocomposite: sono-synthesis and use as a solar photocatalyst for the degradation of DCF as a pharmaceutical pollutant

In this study, novel Ag/AgBr/graphene oxide (GO) nanosheets were successfully preparedviaa facile and fast sonochemical route for the first time, and ultrasound has a key role in the preparation process.

Multimodality imaging of primary CNS lymphoma in immunocompetent patients

Primary central nervous system lymphoma (PCNSL) belongs to the group of extranodal non-Hodgkin's lymphoma, and the management of the disease is radically different from other central nervous system neoplasms. Owing to its varied appearance on imaging, diagnosis of PCNSL can be challenging. The purpose of this pictorial review is to depict the brain findings of PCNSL during initial diagnosis in immunocompetent individuals. Multimodal imaging integrating advanced sequences can facilitate differentiation of PCNSL from other CNS neoplasms.

Scope for energy improvement for hospital imaging services in the USA

Objective

To aid radiologists by measuring the carbon footprint of CT scans by quantifying in-hospital and out-of-hospital energy use and to assess public health impacts.

Method

The study followed a standard life cycle assessment protocol to measure energy from a CT scan then expanding to all hospital electrical energy related to CT usage. In addition, all the fuel energy used to generate electricity and to manufacture the CT consumables was measured. The study was conducted at two hospitals.

Results

The entire life cycle energy for a CT scan was 24–34 kWh of natural resource energy per scan. The actual active patient scan energy that produces the images is only about 1.6% of this total life cycle energy. This large multiplier to get total CT energy is a previously undocumented environmental response to the direct radiology order for a patient CT scan. The CT in-hospital energy related to idle periods, where the machine is on but no patients are being scanned and is 14–30-fold higher than the energy used for the CT image. The in-hospital electrical energy of a CT scan makes up only about 25% of the total energy footprint. The rest is generated outside the hospital: 54–62% for generation and transmission of the electricity, while 13–22% is for all the energy to make the consumables. Different CT scanners have some influences on the results and could help guide purchase of CT equipment.

Conclusions

The transparent, detailed life cycle approach allows the data from this study to be used by radiologists to examine details of both direct and of unseen energy impacts of CT scans. The public health (outside-the-hospital) impact (including the patients receiving a CT) needs to be measured and included.

Systematic review of molecular mechanism of action of negative-pressure wound therapy

BACKGROUND

Negative-pressure wound therapy (NPWT) promotes angiogenesis and granulation, in part by strain-induced production of growth factors and cytokines. As their expression profiles are being unravelled, it is pertinent to consider the mode of action of NPWT at the molecular level.

METHODS

MEDLINE (January 1997 to present), Embase (January 1997 to present), PubMed (no time limit), the Cochrane Database of Systematic Reviews and the Cochrane Controlled Trials Register were searched for articles that evaluated the influence of NPWT on growth factor expression quantitatively.

RESULTS

Sixteen studies met the inclusion criteria. Tumour necrosis factor expression was reduced in acute and chronic wounds, whereas expression of interleukin (IL) 1β was reduced in acute wounds only. Systemic IL-10 and local IL-8 expression were increased by NPWT. Expression of vascular endothelial growth factor, fibroblast growth factor 2, transforming growth factor β and platelet-derived growth factor was increased, consistent with mechanoreceptor and chemoreceptor transduction in response to stress and hypoxia. Matrix metalloproteinase-1, -2, -9 and -13 expression was reduced but there was no effect on their enzymatic inhibitor, tissue inhibitor of metalloproteinase 1.

CONCLUSION

Cytokine and growth factor expression profiles under NPWT suggest that promotion of wound healing occurs by modulation of cytokines to an anti-inflammatory profile, and mechanoreceptor and chemoreceptor-mediated cell signalling, culminating in angiogenesis, extracellular matrix remodelling and deposition of granulation tissue. This provides a molecular basis for understanding NPWT.

Effects of disulfiram on apoptosis in PANC-1 human pancreatic cancer cell line

Pancreatic carcinoma is currently considered as a rapidly progressive and fatal disease, and is typically diagnosed late in its natural course. It is characterized by a poor diagnosis and lack of response to conventional therapy. Recent studies have suggested that disulfiram (DSF), a member of the dithiocarbamate family, may have antitumor activity. This study aimed to evaluate the in vitro effect of DSF on apoptosis in human pancreatic cancerous cell line (PANC-1). PANC-1 cells were cultured and treated with DSF at doses of 5, 10, 13 μM for 24 h and apoptosis was measured. Methylation specific PCR (MS-PCR) and real-time quantitative PCR were carried out to detect the methylation pattern and to estimate the mRNA expression levels of RASSF1A, p21 and Bax. MS-PCR analysis demonstrated that no unmethylated band was apeared in PANC-1 cell line after DSF treatments. The real-time quantitative PCR results showed no significant mRNA expression for RASSF1A (p>0.05); whereas p21 and Bax expression were significantly (p<0.01) enhanced after treatment with DSF. The results of the current study indicated that DSF can induce appoptosis in PANC-1 through p21 and Bax pathway but not through RASSF1A.