Nanoparticle Materials Research Articles

Myconanosynthesis of Copper Oxide Nanoparticles from Talaromyces versatilis against Human Bacterial Pathogens

Globally, there is a clinical problem caused by rising antibiotic resistance. The latest advances in biosynthesis have intrigued scientists and researchers as they investigate its potential against dangerous bacteria. The current emphasis is on myconanosynthesis, copper oxide nanoparticle (CuO NPs) characterization, and antibacterial efficacy. The extracellular biosynthetic approach we employed to create the CuO NPs was friendly to the environment. From agricultural soil samples, Talaromyces versatilis was isolated. They were comparing its 18s rDNA sequencing allowed for identification. Culture filtrate extracts of fungi were used as the reducing agents to synthesis the nanoparticles. XRD and DLS analysis were used to determine copper oxide nanoparticles (CuO NPs) size and material characteristics. When tested against Gram-positive and Gram-negative bacteria from that Salmonella paratyphi, Serratia sp., and Enterococcus fecalis myconanosynthesized copper oxide nanoparticles (CuO NPs) demonstrated a maximal zone of inhibition. Additionally, molecular in vivo research will aid in the creation of a novel wide-spectrum antibacterial drug.

Real-Time Optical Tracking of Protein Corona Formation on Single Nanoparticles in Serum.

The formation of a protein corona, where proteins spontaneously adhere to the surface of nanomaterials in biological environments, leads to changes in their physicochemical properties and subsequently affects their intended biomedical functionalities. Most current methods to study protein corona formation are ensemble-averaging and either require fluorescent labeling, washing steps, or are only applicable to specific types of particles. Here we introduce real-time all-optical nanoparticle analysis by scattering microscopy (RONAS) to track the formation of protein corona in full serum, at the single-particle level, without any labeling. RONAS uses optical scattering microscopy and enables real-time and in situ tracking of protein adsorption on metallic and dielectric nanoparticles with different geometries directly in blood serum. We analyzed the adsorbed protein mass, the affinity, and the kinetics of the protein adsorption at the single particle level. While there is a high degree of heterogeneity from particle to particle, the predominant factor in protein adsorption is surface chemistry rather than the underlying nanoparticle material or size. RONAS offers an in-depth understanding of the mechanisms related to protein coronas and, thus, enables the development of strategies to engineer efficient bionanomaterials.

Conversational AI Tools for Environmental Topics: A Comparative Analysis of Different Tools and Languages for Microplastics, Tire Wear Particles, Engineered Nanoparticles and Advanced Materials

Artificial intelligence gained a surge in popularity through the release of conversational artificial intelligence tools, which enable individuals to use the technology without any prior knowledge or expertise in computational science. Researchers, content writers, as well as curious minds may use these tools to investigate any topics in question. Environmental topics, as one of the current public concerns, are covered by many different kinds of media, indicating a broad public interest. To assess the possibility of using these tools in environmental-related content writing or research, we tested the capabilities of conversational artificial intelligence tools on selected environmental topics. In particular, we tested different tools (ChatGPT, Microsoft Bing, Google Bard) and different languages (English, Spanish, Korean, German, Turkish and Chinese) via using selected questions and compared the answers with each other. Our results suggest that conversational artificial intelligence tools may provide satisfactory and comprehensive answers; however, we found some of the statements debatable and texts still need to be reviewed by an expert. Selected tools may offer specific advantages, such as providing references, although certain issues may need to be checked for each tool. The usage of different languages may provide additional points within the content; however, this does not necessarily imply that these new facets arise solely from utilizing different languages, since new aspects may also be attributed to the ‘randomness of the generated answers’. We suggest asking the same question several times as the tools mostly generate random answers each time, especially for ChatGPT, to obtain a more comprehensive content.

TiO2 loaded on glycidol functionalized poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) nanobiocomposite film for photocatalytic and antibacterial activities

The rapid acceleration of industrialization and urbanization has exacerbated water pollution, which is primarily caused by the presence of highly toxic, non-biodegradable contaminants in industrial waste and effluents. In response to this urgent issue, a novel nanobiocomposite film with titanium dioxide (TiO2) loaded onto a poly(3-hydroxybutyrate-co-18 mol% 3-hydroxyhexanoate) (18PHBH) matrix was developed to serve as an effective dual-function material with photocatalytic and antibacterial properties. Through Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR), Diffuse reflectance ultraviolet-visible (DRUV-Vis), Scanning Electron Microscope (SEM), and X-ray diffraction (XRD) analyses, the physicochemical properties of the TiO2/Gly/18PHBH nanobiocomposite film were exhaustively characterized, revealing effective TiO2 loading and uniform distribution on the film's surface. The film exhibited extraordinary photocatalytic degradation of methylene blue (MB) dye, with the 5TiO2/Gly/18PHBH film demonstrating the greatest efficiency. In addition, antibacterial testing revealed that the film was effective against 99.8 % of Staphylococcus aureus and 96.9 % of Pseudomonas aeruginosa. These results demonstrate the potential of polyhydroxyalkanoate-based films as exceptional nanoparticle matrices and position the 5TiO2/Gly/18PHBH film as a versatile candidate for applications in photocatalysis and antibacterial interventions, providing innovative solutions to critical environmental challenges.

A review on toxicity of nanomaterials in agriculture: Current scenario and future prospects.

Phytonanotechnology plays a crucial part in the production of good quality and high-yield food. It can also alter the plant's production systems, hence permitting the efficient, controlled and stable release of agrochemicals such as fertilizers and pesticides. An advanced understanding of nanomaterials interaction with plant responses like localization and uptake, etc. could transfigure the production of crops with high disease resistance and efficient nutrients utilization. In agriculture, the use of nanomaterials has gained acceptance due to their wide-range applications. However, their toxicity and bioavailability are the major hurdles for their massive employment. Undoubtedly, nanoparticles positively influence seeds germination, growth and development, stress management and post-harvest handling of vegetables and fruits. These nanoparticles may also cause toxicity in plants through oxidative stress by generation of excessive reactive oxygen species thus affecting the cellular biomolecules and targeting different channels. Nanoparticles have shown to exert various effects on plants that are mainly affected by various attributes such as physicochemical features of nanomaterials, coating materials for nanoparticles, type of plant, growth stages and growth medium for plants. This article discusses the interaction, accretion and toxicity of nanomaterials in plants. The factors inducing nanotoxicity and the mechanisms followed by nanomaterials causing toxicity are also instructed. At the end, detoxification mechanism of plant is also presented.

A brief overview on iron oxide nanoparticle synthesis, characterization, and applications

Hematite (α-Fe2O3), magnetite (Fe3O4), and maghemite (γ-Fe2O3) are only a few of the polymorphic forms of iron oxide, a mineral component. Hematite and maghemite, solid propulsion technology nanoparticulate materials, perform exceptionally well during the thermal degradation of ammonium perchlorate. Due to their larger particle size, more active sites, and increased surface area, which encourages more gas adsorption during thermal oxidation reactions, metallic iron oxide nanoparticles have an enhanced catalytic impact. Currently, a variety of techniques, including co-precipitation, sol–gel, microemulsion, and thermal decomposition, can be used to create metallic iron nanoparticles. The literature contains information on these synthetic processes, but there is a dearth of information on nanoparticulate oxides and the methods used to characterise them. The methods for generating nanoparticulate iron oxides, the key features of the various characterization techniques, as well as information regarding the breakdown properties of these nanomaterials are presented in this concise review based on scientific papers, containing data from the last two decades. Transmission electron microscopy, scanning electron microscopy, X-ray powder diffraction, and Fourier transform infrared spectroscopy can all be used to describe the morphologies and structures of iron oxides. Physical adsorption techniques are typically used to determine the textural qualities.

Influence of Colloidal Additivation with Surfactant‐Free Laser‐Generated Metal Nanoparticles on the Microstructure of Suction‐Cast Nd–Fe–B Alloy

Development of new powder feedstocks using nanoparticles (NPs) has the potential to influence the microstructure of as‐built parts and overcome the limitations of current powder‐based additive manufacturing (AM) techniques. The focus of this study is to investigate the impact of NP‐modified magnetic microparticle powder feedstock on the microstructure of suction‐cast Nd–Fe–B‐based alloys. This particular casting method has been recognized for its ability to replicate, to some extent, the melting and rapid solidification stages inherent to metal powder‐based AM techniques such as powder bed fusion using a laser beam. Two types of NP materials, Ag and ZrB2, are used, and their effects on the grain size distribution and dendritic structures are evaluated after suction casting. Ag NPs result in smaller, more uniform grain sizes. ZrB2 NPs result in uniformly distributed grain sizes at much lower mass loadings. The results show that feedstock powder surface modification with low‐melting‐point metal NPs can improve permanent magnets’ microstructure and magnetic properties, at below 1 vol%, equal to submonolayer surface loads. Herein, the potential of using NPs to develop new powder feedstocks for AM is highlighted, significantly improving the final part's properties.

CONFORMATIONAL STRUCTURE OF AN ADSORBED POLYELECTROLYTE ON A NANOPARTICLE WITH LOW CONDUCTIVITY IN AN ALTERNATING ELECTRIC FIELD

An analytical form of the model of the quasi-equilibrium conformational structure of the units of the Gaussian chain of a polyelectrolyte adsorbed on a nanospheroid witha relatively low electrical conductivity (undoped semiconductor) polarized in an external harmonically varying quasi-static electric field with a frequency significantly lower than the plasma frequency of the nanoparticle material is proposed. Variants ofthe model are discussed that go beyond the scope of the quasi-static approximation, i.e., take into account the effects of delay, the manifestation of which will be noticeable in the case of sufficiently extended nanostructures. Electrically induced conformational changes of generally neutral polyampholytic polypeptides on the surface of a spherical germanium nanoparticle in a static or alternating external electric field have been studied by molecular dynamics. In a static electric field, in the case of asmall distance between the charged units in the polyampholyte, a large number of macrochain loops were formed, elongated in the direction of the polarization axis of the nanoparticle. If the distance between the oppositely charged amino acid residues of the polypeptide exceeded the diameter of the nanoparticle, the charged units were mainly localized in the oppositely charged subpolar regions of the polarized germanium nanoparticle. In an alternating electric field, a girdle polyampholyte edge was formed in the equatorial region of the nanoparticle, the macrochain links of which were desorbed from the surface with an increase in the amplitude of the polarizing alternating electric field.

Deep learning models for assisted decision-making in performance optimization of thin film nanocomposite membranes

The performance of thin film nanocomposite nanofiltration membranes (TFNNMs) is frequently constrained by the trade-off between the permeability and rejection/selectivity of the membrane. In this study, to provide assisted decision-making in performance optimization of TFNNMs, we utilized a dataset comprising four categories of impact parameters, including membrane manufacturing conditions, membrane performance, nanoparticle material performance, and organic solvent nanofiltration conditions, and trained a dual output neural network (D-ANN) to simulate the relative permeability (RP) and relative rejection/selectivity (RR) of TFNNMs. The D-ANN model outperformed conventional machine learning models and single-output neural networks in predicting both RP and RR, indicating its potential in uncovering correlations between output variables. The Shapley additive explanation method was employed to elucidate the contribution and marginal effects of each influencing parameter on RP and RR, among them, NP size, NP loading, water contact angle, and chloride concentration were influential parameters that are important for both RP and RR. Based on the D-ANN model, we extrapolated two sets of unknown data and achieved good predictive performance (RP-error <15%, RS-error <7% for most sample points). Furthermore, we observed that the predicted performance of the data points rarely exceeded the actual performance, indicating a low probability of overestimation. Based on these premises, the influential parameters can be optimized using the D-ANN model, which ultimately led to exceptional performance for a total of 22 datasets in four pairs of organic solvent and solute purifications guided by the D-ANN model. This study demonstrates that the D-ANN model can provide decisions for the performance optimization of TFNNMs while highlighting the enhancement of nanofiltration performance by thin film nanocomposites, significantly facilitating the design of high-performance TFNNMs.

Correlating oxygen functionalities and electrochemical durability of carbon supports for electrocatalysts

Achieving high durability of the polymer electrolyte membrane (PEM) fuel cell catalysts remains a major challenge. While most of the research focuses on the active phase, carbon support remains overlooked. In this study durability of carbon support materials for Pt alloy nanoparticles is critically evaluated. First graphene derivative (GD) based carbon supports with different chemical properties are prepared and utilized along with widely used commercial carbon black (CB) material. High-temperature electrochemical accelerated degradation tests (HT-ADTs) combined with X-ray photoelectron spectroscopy (XPS) show that the total amount of oxygen functionalities, the type of oxygen functionalities, and sp2 carbon content play a crucial role in carbon support durability. The observations were confirmed with the direct online measurements of carbon corrosion via an advanced in-situ technique – an electrochemical cell coupled with a mass spectrometer (EC-MS). We report that increasing the content of sp2 carbon and decreasing carboxyl functional groups have the most beneficial effect on stability. The study provides important guidelines for tailoring the carbon support properties and their relationship to the durability of the electrocatalyst, which could be crucial for producing more stable catalysts and achieving the Department of Energy's fuel cell system lifetime targets. Moreover, the innovative carbon design approach presented here could be applied in other fields such as batteries, supercapacitors, sensors and others.

Predictive modelling of thermal conductivity in single-material nanofluids: a novel approach

BackgroundThis research introduces a novel approach for modelling single-material nanofluids, considering the constituents and characteristics of the fluids under investigation. The primary focus of this study was to develop models for predicting the thermal conductivity of nanofluids using a range of machine learning algorithms, including ensembles, trees, neural networks, linear regression, Gaussian process regressors, and support vector machines.The main body of the abstractTo identify the most relevant features for accurate thermal conductivity prediction, the study compared the performance of established feature selection algorithms, such as minimum redundancy maximum relevance, Ftest, and RReliefF, a newly proposed feature selection algorithm. The novel algorithm eliminated features lacking direct implications for fluid thermal conductivity. The selected features included temperature as a thermal property of the fluid itself, multiphase features such as volume fraction and particle size, and material features including nanoparticle material and base fluid material, which could be fixed based on any two intensive properties. Statistical methods were employed to select the features accordingly.ResultsThe results demonstrated that the novel feature selection algorithm outperformed the established approaches in predicting the thermal conductivity of nanofluids. The models were evaluated using fivefold cross-validation, and the best model was the model based on the proposed feature selection algorithm that exhibited a root-mean-squared error of validation of 1.83 and an R-squared value of 0.94 on validation set. The model achieved a root-mean-squared error of 1.46 and an R-squared value of 0.97 for the test set.ConclusionsThe developed predictive model holds practical significance by enabling nanofluids' numerical study and optimisation before their creation. This model facilitates the customisation of conventional fluids to attain desired fluid properties, particularly their thermal properties. Additionally, the model permits the exploration of numerous nanofluid variations based on permutations of their features. Consequently, this research contributes valuable insights to the design and optimisation of nanofluid systems, advancing our understanding and application of thermal conductivity in nanofluids and introducing a novel and methodological approach for feature selection in machine learning.

Modelling of nanosensors based on localised surface plasmon resonance

ABSTRACT To design nanosensors based on localised surface plasmon (LSP), a structure is considered consisting of metal nanoparticles and study the influence of nanoparticles size, material, geometry, and background refractive index (RI) on its performance. We propose a nanosensor based on nanoplasmonic and investigate its sensitivity. The boundary element method is employed to calculate the extinction cross-section and sensitivity of the proposed sensor. We study the effect of various parameters on LSP resonance. Our calculations about extinction, scattering, and absorption spectra have been compared with experimental data. According to the comparison, it is deduced the boundary element method provides acceptable results. It is shown that the proposed nanosensor is very sensitive to the variation of sample RI. Moreover, it is possible to adjust the required spectral range by changing the geometry and material of nanoparticles. Here, the highest sensitivity is obtained for cubic nanoparticles made of silver.

Hydrodynamic and heat transfer investigations of multi-phase nanofluid flow in the phase change material of silver nanoparticles dispersed in water/wax-paraffin

Using phase change materials (PCM) significantly contributes to thermal energy storage. Due to their high melting heat, PCM is a material that can store heat without causing noticeable changes in temperature (Temp). Paraffin is one of the types of PCM, and compared to other PCMs, it is more resistant and has a lower price, and at the same time, it has higher safety. In the upcoming research, silver nanoparticles (Ag-NPs) with different atomic ratios (10, 20, 30, and 50%) have been evaluated on the thermal behavior of water/wax-paraffin hybrid nanofluid (NF) in a Fe nanochannel using the molecular dynamics (MD) simulation. The results illustrated that NPs positively and significantly affect the thermal conductivity (TC) of water-paraffin wax (PW) /Ag-NPs hybrid NF. By increasing the atomic ratio of Ag-NPs from 10 to 50%, the value of TC has increased from 0.381 to 0.476 W/m.K Also, the addition of Ag-NPs by 50% has led to an increase in the maximum values of density (Den.), Temp, and velocity (Vel) to 0.1425 atom/Å3, 3.57E-4 Å/fs, and 488.42 K.

Nanotechnology-based theranostic and prophylactic approaches against SARS-CoV-2.

SARS-CoV-2 (COVID-19) pandemic has been an unpredicted burden on global healthcare system by infecting over 700 million individuals, with approximately 6 million deaths worldwide. COVID-19 significantly impacted all sectors, but it very adversely affected the healthcare system. These effects were much more evident in the resource limited part of the world. Individuals with acute conditions were also severely impacted. Although classical COVID-19 diagnostics such as RT-PCR and rapid antibody testing have played a crucial role in reducing the spread of infection, these diagnostic techniques are associated with certain limitations. For instance, drawback of RT-PCR diagnostics is that due to degradation of viral RNA during shipping, it can give false negative results. Also, rapid antibody testing majorly depends on the phase of infection and cannot be performed on immune compromised individuals. These limitations in current diagnostic tools require the development of nanodiagnostic tools for early detection of COVID-19 infection. Therefore, the SARS-CoV-2 outbreak has necessitated the development of specific, responsive, accurate, rapid, low-cost, and simple-to-use diagnostic tools at point of care. In recent years, early detection has been a challenge for several health diseases that require prompt attention and treatment. Disease identification at an early stage, increased imaging of inner health issues, and ease of diagnostic processes have all been established using a new discipline of laboratory medicine called nanodiagnostics, even before symptoms have appeared. Nanodiagnostics refers to the application of nanoparticles (material with size equal to or less than 100nm) for medical diagnostic purposes. The special property of nanomaterials compared to their macroscopic counterparts is a lesser signal loss and an enhanced electromagnetic field. Nanosize of the detection material also enhances its sensitivity and increases the signal to noise ratio. Microchips, nanorobots, biosensors, nanoidentification of single-celled structures, and microelectromechanical systems are some of the most modern nanodiagnostics technologies now in development. Here, we have highlighted the important roles of nanotechnology in healthcare sector, with a detailed focus on the management of the COVID-19 pandemic. We outline the different types of nanotechnology-based diagnostic devices for SARS-CoV-2 and the possible applications of nanomaterials in COVID-19 treatment. We also discuss the utility of nanomaterials in formulating preventive strategies against SARS-CoV-2 including their use in manufacture of protective equipment, formulation of vaccines, and strategies for directly hindering viral infection. We further discuss the factors hindering the large-scale accessibility of nanotechnology-based healthcare applications and suggestions for overcoming them.

Poly(m-phenylene isophthalamide) (PMIA) membranes as a support for WO3/g-C3N4 for the degradation of diclofenac in water

One of the obstacles hampering large-scale semiconductor photocatalysis is the requirement for the recovery and reuse of photocatalytic nanoparticles. Polymeric membranes are viable and practical support materials for photocatalytic nanoparticles to result in multifunctional photocatalytic membranes. PMIA membranes were applied as support for photocatalytic WO3/g-C3N4 nanoparticles and evaluated for the photodegradation of diclofenac (DFC). The PMIA photocatalytic membranes were prepared using the conventional phase inversion method in which the photocatalyst was embedded into the membranes. SEM, XRD, XPS, and FTIR analysis confirmed the incorporation of WO3/g-C3N4 into PMIA membranes. The contact angle of the PMIA/WGC membranes was reduced from 56.3° for the pure PMIA membrane to 37.5°, and porosity was increased from 74.7 up to 85.3%. The photocatalytic membranes were irradiated in the visible light region. M5, which contains 2% of WO3/g-C3N4 in the PMIA membrane, presented the highest degradation of DFC (85%) after 150 min compared to the other membranes. The degradation performance of DFC using PMIA/WGC was mainly driven by hydroxyl radicals and singlet oxygen. The photodegradation performance within 10 cycles had a difference of less than 5%, indicating the stability of membranes. The PMIA membranes are viable photocatalyst support materials with potential dual functionality in water treatment.

In vitro and in vivo evaluation of immune response of poly(lactic acid) nanoparticles with different end groups

Poly(lactic acid) (PLA) has excellent properties of biodegradability and biocompatibility, which is a US Food and Drug Administration (FDA) approved biopolymer for the preparation of safe and effective vaccines, drugs, and gene delivery systems. However, there still exists a great problem whether and how the end group affects the immune response of PLA vaccines. Therefore, the aim of this study was to evaluate the in vitro and in vivo of immune response of PLA nanoparticles (NPs) with carboxyl (COOH) and ester (COOR) end groups. In vitro experiments suggested COOH NPs could promote the higher phagocytosis and activation of bone marrow dendritic cells (BMDCs) with a lower cytotoxicity. In vivo experiments showed that COOR NPs and COOH NPs could strongly elicit IgG, IgG1, and IgG2a responses both in the short and long-terms. However, the highest T cell and B cell activation, and central memory T cells response was induced by COOH NPs. In addition, the COOH NPs could significantly enhance splenocytes proliferation and cytokines secretion. Thus, the PLA with the COOH end group shows greater potential as efficient carrier materials of NPs for enhancing cellular and humoral immune responses.

Holey Graphene/Sulfur Composite Cathodes Via Coupling with Ferroelectric Nanoparticles for Sustainable and High-Capacity Lithium-Sulfur Batteries

Lithium-Sulfur (Li-S) batteries have been considered promising for high energy density batteries to be used in electronic devices, electric vehicles (EV) and aircrafts which are becoming more prevalent. In addition, Li-S batteries have high theoretical capacity of 1,675 mAh/g in comparison to LIBs of 300 mAh/g. Although Li-S batteries are in great demand, their development is still plagued by rapid capacity fading mainly stemming from polysulfide shuttle. To improve the electrochemical performance of the cathode in Li-S batteries, holey graphene (hG) and carbon-based materials are employed. Holey graphene (HG) contains conductive skeletons as electron transfer paths and abundant mesoporous for longitudinal transport of ions. This architecture ensures efficient charge delivery throughout a thick electrode and maximizes electrode utilization, achieving high-rate and high-capacity energy storage. The coupling of a highly polarized ferroelectric nanoparticles materials (FNPs) layer is an approach that can be used to reduce the characteristic polysulfide shuttling in Li-S batteries. This coupling helps to build up an internal electric field and induces macroscopic charge separations on the surface of the cathode of Li-S batteries. The dry compression manufacturing process facilitates bonding between Sulfur and ferroelectric nanoparticles to generate a more stable cathode. In the present report we are presenting preliminary results of cathode having composition S47.5(FNPs)5hG47.5 in terms of structure, surface morphology and electrochemical performance. A reversible capacity of ~1,400 mAh/gs has been achieved with high coulombic efficiencies (> 87%) and low-capacity fading rate of 0.016% per cycle upto 60 cycles. The active sulfur mass loading ranging from 5.72 mgs/cm2 to 7.01 mgs/cm2 allows maximum high areal capacity up to ~10 mAh/cm2 and superior rate capability for 0.2mA/cm2 and 0.5 mA/cm2. These results suggest sustainable and high-yielding Li–S batteries can be obtained for potential commercial applications.

Actinide Ion (Americium-241 and Uranium-232) Interaction with Hybrid Silica-Hyperbranched Poly(ethylene imine) Nanoparticles and Xerogels.

The binding of actinide ions (Am(III) and U(VI)) in aqueous solutions by hybrid silica-hyperbranched poly(ethylene imine) nanoparticles (NPs) and xerogels (XGs) has been studied by means of batch experiments at different pH values (4, 7, and 9) under ambient atmospheric conditions. Both materials present relatively high removal efficiency at pH 4 and pH 7 (>70%) for Am(III) and U(VI). The lower removal efficiency for the nanoparticles is basically associated with the compact structure of the nanoparticles and the lower permeability and access to active amine groups compared to xerogels, and the negative charge of the radionuclide species is formed under alkaline conditions (e.g., UO2(CO3)34- and Am(CO3)2-). Generally, the adsorption process is relatively slow due to the very low radionuclide concentrations used in the study and is basically governed by the actinide diffusion from the aqueous phase to the solid surface. On the other hand, adsorption is favored with increasing temperature, assuming that the reaction is endothermic and entropy-driven, which is associated with increasing randomness at the solid-liquid interphase upon actinide adsorption. To the best of our knowledge, this is the first study on hybrid silica-hyperbranched poly(ethylene imine) nanoparticle and xerogel materials used as adsorbents for americium and uranium at ultra-trace levels. Compared to other adsorbent materials used for binding americium and uranium ions, both materials show far higher binding efficiency. Xerogels could remove both actinides even from seawater by almost 90%, whereas nanoparticles could remove uranium by 80% and americium by 70%. The above, along with their simple derivatization to increase the selectivity towards a specific radionuclide and their easy processing to be included in separation technologies, could make these materials attractive candidates for the treatment of radionuclide/actinide-contaminated water.

On-Surface Carbon Nitride Growth from Polymerization of 2,5,8-Triazido-s-heptazine.

Carbon nitrides have recently come into focus for photo- and thermal catalysis, both as support materials for metal nanoparticles as well as photocatalysts themselves. While many approaches for the synthesis of three-dimensional carbon nitride materials are available, only top-down approaches by exfoliation of powders lead to thin-film flakes of this inherently two-dimensional material. Here, we describe an in situ on-surface synthesis of monolayer 2D carbon nitride films as a first step toward precise combination with other 2D materials. Starting with a single monomer precursor, we show that 2,5,8-triazido-s-heptazine can be evaporated intact, deposited on a single crystalline Au(111) or graphite support, and activated via azide decomposition and subsequent coupling to form a covalent polyheptazine network. We demonstrate that the activation can occur in three pathways, via electrons (X-ray illumination), via photons (UV illumination), and thermally. Our work paves the way to coat materials with extended carbon nitride networks that are, as we show, stable under ambient conditions.

Synthesis of a novel magnetic chitosan-mediated GO dual-template imprinted polymer for the simultaneous and selective removal of Cd(Ⅱ) and Ni(Ⅱ) from aqueous solution

With the uptrend of the energy industry and Cadmium-Nickel batteries, the development and comprehensive utilization of highly efficient adsorptive material for simultaneous and selective capture of Cd(II) and Ni(II) ions are of great significance, but formidable. Herein, a dual-template imprinted nanoparticle material (MCTS@GO@DIIP) was synthesized on GO slice structure coated with MnFe2O4 magnetic chitosan film via surface ion-imprinted technique for selective removal of Cd(II) and Ni(II) from aqueous solution. The material was based on ion-imprinted condensates of chitosan, L-histidine and GO as ion recognition layer with Cd(II) and Ni(II) as dual-template ions, L-histidine as monomer and glutaraldehyde as crosslinking agent. The obtained MCTS@GO@DIIP was characterized by SEM, FT-IR, XRD and TGA to study the surface structure, group distribution, crystal structure and thermal property. The adsorption experiments exhibited the MCTS@GO@DIIP achieved adsorption equilibrium for Cd(II) and Ni(II) ions in 30 min with the maximum adsorption capacities of 33.91 and 39.35 mg g−1, respectively. The adsorption process was agreed with the pesudo-second-order kinetic model and Langmuir isotherm adsorption model, proclaiming monolayer chemisorption on homogeneous sites was the rate-limiting step. Meanwhile, the MCTS@GO@DIIP also showed a specificity recognition ability for Cd(II) and Ni(II) using analogues Co(II), Zn(II), Mg(II) and Pb(II) as interference. Moreover, the MCTS@GO@DIIP based MSPE could extract approximately 95.5–104.6% of Cd(II) with a RSD of 3.34–8.27%, and 90.0–103.4% of Ni(II) with a RSD of 3.21–7.03% in real water samples, which indicated the polymer had the high stability and reproducibility and presented itself a promising material for the Cd(II) and Ni(II) adsorption and detection from aqueous solution.