Nanostructured Materials Research Articles

Exploring the Superalkali Regime: Ab Initio Analysis of FnMn+1+ Cationic Clusters (M = Li, Na, K; n = 1-7)

Abstract Superalkalis with low ionization energies (IEs) has properties mimicking those of alkali atoms, therefore serving as potentially useful elements for constructing innovative nanostructured materials. Ab initio techniques were successfully utilized for investigating entirely new classes of linear metallic superalkali cationic clusters, FnMn+1+ cations (M = Li, Na and K and n = 1-7) using second-order Møller- Plesset perturbation theory (MP2). We have demonstrated a connection between the core atoms and the structural characteristics of such clusters by analyzing the NBO charge, bond distances and vertical electron affinity (EAv). The linear FnLin+1+, FnNan+1+ and FnKn+1+ (n = 1-7) cations have exceptionally low vertical electron affinities under the range of 3.61- 2.35 eV, 3.26- 2.19 eV and 2.84- 1.80 eV, respectively, and consequently, they ought to be characterized as novel superalkali clusters.

Computational homogenization for aerogel-like polydisperse open-porous materials using neural network-based surrogate models on the microscale

Abstract The morphology of nanostructured materials exhibiting a polydisperse porous space, such as aerogels, is very open porous and fine grained. Therefore, a simulation of the deformation of a large aerogel structure resolving the nanostructure would be extremely expensive. Thus, multi-scale or homogenization approaches have to be considered. Here, a computational scale bridging approach based on the $$\hbox {FE}^2$$ FE 2 method is suggested, where the macroscopic scale is discretized using finite elements while the microstructure of the open-porous material is resolved as a network of Euler–Bernoulli beams. Here, the beam frame based RVEs (representative volume elements) have pores whose size distribution follows the measured values for a specific material. This is a well-known approach to model aerogel structures. For the computational homogenization, an approach to average the first Piola–Kirchhoff stresses in a beam frame by neglecting rotational moments is suggested. To further overcome the computationally most expensive part in the homogenization method, that is, solving the RVEs and averaging their stress fields, a surrogate model is introduced based on neural networks. The network’s input is the localized deformation gradient on the macroscopic scale and its output is the averaged stress for the specific material. It is trained on data generated by the beam frame based approach. The effiency and robustness of both homogenization approaches is shown numerically, the approximation properties of the surrogate model is verified for different macroscopic problems and discretizations. Different (Quasi-)Newton solvers are considered on the macroscopic scale and compared with respect to their convergence properties.

Integrating Ni-MOF/g-C3N4/chitosan derived S-scheme photocatalyst for efficient visible light photodegradation of tetracycline and antibacterial activities.

Nickel MOF (Ni-MOF) nanoparticles were successfully anchored onto a polymeric graphitic carbon nitride (g-C3N4) and Chitosan nanostructure (NS) using an eco-friendly and straightforward synthesis method. These newly fabricated photocatalysts were thoroughly characterized with standard techniques, revealing that the nanoscale Ni-MOF particles were uniformly deposited on the sheet-like g-C3N4 matrix. This configuration demonstrated excellent antimicrobial properties and outstanding photodegradation of tetracycline hydrochloride under visible light exposure. The MOF@GC photocatalyst exhibited robust bactericidal activity against pathogens like Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Additionally, it achieved superior visible-light-driven degradation of tetracycline in a significantly shorter time compared to other studies, with approximately 96% of the tetracycline being degraded in just 70min under visible light. These findings suggest that the effective deposition of Ni-MOF onto the g-C3N4 structure reduces the recombination rate of photogenerated electrons and holes, thereby enhancing the photocatalytic efficiency of pure g-C3N4 under visible light. The proposed catalytic mechanism, informed by valence band (VB) and conduction band (CB) data from cyclic voltammetry measurements, further supports this conclusion. The MOF@GC photocatalyst is a promising nanostructured material for antimicrobial applications and visible-light-driven photocatalysis.

Dimensional effect on phonon frequency vibrations of nanostructured materials

Dimensional effect on phonon frequency vibrations of nanostructured materials

Rietveld Refinement of Electron Diffraction Patterns of Nanocrystalline Materials Using MAUD: Two-Beam Dynamical Correction Implementation and Applications

Nanocrystalline (NC) materials have widespread industrial usage. X-ray and neutron diffraction techniques are primary tools for studying the structural and microstructural features of NC materials. Selected area electron diffraction (SAED) patterns collected using a transmission electron microscope (TEM) on polycrystalline nanostructured materials, featuring nested rings, that are analogous to Debye–Scherrer patterns, possess similar potentials to aid materials characterisation. The utility of SAED patterns is further enhanced by the possibility of applying crystallographic approaches, like full pattern fitting procedures, based on Rietveld refinement algorithms, enabling the evaluation of material features, such as crystallite size, lattice distortions, defect structures, and the presence of secondary phases even from very small volume scale. In this paper, we have discussed the possibilities afforded by a Rietveld code applied to SAED patterns of NC materials, including the mathematical implementation of the two-beam dynamical correction model in MAUD software (version 2.9996), and a critical discussion of the results obtained on different NC materials.

Synergistic Impacts of Bismuth Ferrite and Graphitic Carbon Nitride Nanomaterials on Soil Properties: A Path Toward Enhance Soil Properties

ABSTRACT The use of nanostructured materials in soil sustainability is advancing in recent time. The aim of this research is to investigate the impact of synthesized nanostructured photocatalysts, including metal-free graphitic carbon nitride (g-C3N4), bismuth ferrite (BiFeO3), magnesium-doped bismuth ferrite (Bi0.7Mg0.3FeO3), and a heterojunction of g-C3N4/BiFeO3, on various soil properties. Photocatalysts were synthesized using pyrolysis, sol–gel, and coprecipitation methods and were systematically characterized using powdered X-ray diffraction analysis, Fourier transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis. Soil samples for the experiment were collected, prepared, and were subjected to different catalyst treatments and exposure to sunlight durations. Experiment was conducted following a completely randomized design with three repetitions. Results revealed that the photocatalysts significantly altered some of the soil properties. The study found that the photocatalysts significantly influenced soil pH (from 6.91 in control to 6.44 in BiFeO3) and exchangeable acidity on a short-term (from 1.42 cmolkg−1 in 0 h to 0.82 cmolkg−1 in 500 h) after application. Notably, g-C3N4 demonstrated nitrogen fixation potential (9.1 gkg−1 compared to 1.8 gkg−1 in control), significantly increasing total nitrogen levels in the soil and demonstrated the highest cation exchange capacity (7.23 cmolkg−1 compared to 6.71 cmolkg−1 in control). In conclusion, the novelty of these findings provide important insights into the potential of synthesized nanostructured photocatalysts in the modification of soil properties. It highlights the importance of exposure time and catalyst levels in accomplishing desired modifications in soil characteristics, offering a basis for further investigations into agricultural and soil remediation applications of these photocatalytic nanostructured materials.

A Simple Micro/Nano‐Porous Structure With Day‐Night Dynamic Thermal Control and High Thermal Conductivity for Enhanced Thermoelectric Power Generation

AbstractThe main challenge of an all‐weather power generation system that combines radiative cooling and thermoelectricity is to dynamically regulate the temperature sources at both ends of the thermoelectric device. Previous studies have used stacked and flipped structures, thermochromic materials, and other methods. However, the above methods have complex structures, low thermal conductivity efficiency, and difficult‐to‐prepare materials. Therefore, in this paper, a porous structure of carbon nanomaterials with simple preparation, dynamically adjustable heat source during day and night, and high thermal conductivity in combination with thermoelectric devices with ultra‐high electrical efficiency is proposed. The structure has a solar spectrum absorption of 98.6%. Graphene film is used as a substrate material to enhance the longitudinal thermal conductivity. Experiments showed that the maximum voltage during day and night can reach 847 and 106 mV, the maximum temperature difference is 12.63 and 1.42 °C, and the maximum power density is 2112.96 mW m−2 when combined with thermoelectric devices. Simulation models are used to analyze the environmental sensitivity of power generation units and to predict their power generation potential. This work provides superior photothermal/radiative cooling structures combined with thermoelectric elements for efficient all‐weather energy conversion and power output.

Effect of Optical Properties on Ag/MWCNTs Nanostructured Materials Prepared by Laser Physical Method for the Removal of Methylene Orange dye: Kinetic and Isotherm Studies

Effect of Optical Properties on Ag/MWCNTs Nanostructured Materials Prepared by Laser Physical Method for the Removal of Methylene Orange dye: Kinetic and Isotherm Studies

Glow Discharge Optical Emission Coded Aperture Spectroscopy.

Glow discharge optical emission spectrometry (GDOES) allows fast and simultaneous multielemental analysis directly from solids and depth profiling down to the nanometer scale, which is critical for thin-film (TF) characterization. Nevertheless, operating conditions for the best limits of detection (LODs) are compromised in lieu of the best sputtering crater shapes for depth resolution. In addition, the fast transient signals from ultra-TFs do not permit the optimal sampling statistics of bulk analysis such that LODs are further compromised. Furthermore, commercial GDOES instruments rely on a slit-based light dispersion that favors high spectral resolution at the expense of light throughput. Here, a new technique called glow discharge optical emission coded aperture spectrometry (GOCAS) is shown to allow both a higher spectral resolution and higher light throughput by using a coded aperture (CA) with multiple thin slits at the spectrograph's entrance to measure the convoluted spectra and compressed sensing (CS) algorithms to recover the deconvoluted spectra from the full field of view. The effects of CA characteristics on spectral reconstruction fidelity were studied and showed the best fidelity for smaller slits, 50% transmittance, and wider CA with a higher number of slits. In addition, Shearlet enhanced snapshot compressive imaging (SeSCI)GPU showed the best performance of the CS algorithms studied, including SeSCICPU, two-step iterative shrinkage/thresholding (TwIST), and alternating direction method of multipliers total variation minimization (ADMM-TV). Moreover, GOCAS is shown to be very robust against increasing detector Gaussian noise. Finally, standard reference materials are used to show up to ∼30× improved S/N and an order-of-magnitude improved LODs, at the fastest acquisition times (fraction of a ms), which has the potential to be transformative for depth profiling of nanostructured materials.

Optimizing high-pressure sliding process parameters for grain refinement and enhanced mechanical properties using response surface methodology approach

Lightweight technology has emerged as a crucial focus within transportation machinery manufacturing, driving the pursuit for materials with exceptional strength-to-weight ratios. To address this need, ultra-fine grain (< 500 nm) and nanostructured (< 100 nm) materials are being developed using severe plastic deformation (SPD) techniques, particularly in confined flat sheets. This study focuses on the Multi Pass Incremental Feed High-Pressure Sliding (MPIF-HPS) technique, which offers significant potential for industries such as aerospace and automotive by enhancing the mechanical properties of materials like AA 7075 alloy. This work investigates the development and characterization of ultrafine-grained materials through SPD, focusing on the correlation between ultrafine grain structures and their distinct properties. Utilizing MPIF-HPS processes and design of experiment (DoE) methodologies, the study evaluates the evolution of fine-grained microstructures and the enhancement of boundary characteristics with increasing strain in AA 7075 alloy. The grain size was refined from 600 µm to 6 µm, indicating significant microstructural improvement. Concurrently, mechanical properties were notably enhanced, with hardness increasing from 102 to 165 HV, yield strength from 250 to 605 MPa, and ultimate tensile strength (UTS) from 400 to 650 MPa. The study systematically optimized processing parameters, including pressure, sliding distance, and number of passes, to determine the ideal conditions for grain refinement and mechanical performance. These findings underscore the critical role of strain-induced boundary characteristics in determining the material's final properties, paving the way for tailored solutions to meet specific industrial requirements.

Formation of Nanowindow between Graphene Oxide and Carbon Nanohorn Assisted by Metal Ions.

This study presents a novel nanostructured material formed by inserting oxidized carbon nanohorns (CNHox) between layered graphene oxide (GO) nanosheets using metal ions (M) from nitrate as intermediates. The resulting GO-CNHox-M structure effectively mitigated interlayer aggregation of the GO nanosheets. This insertion strategy promoted the formation of nanowindows on the surface of the GO sheets and larger mesopores between the GO nanosheets, improving material porosity. Characterization revealed successful CNHox insertion, which increased interlayer spacing and reduced GO stacking. The GO-CNHox-Ca exhibited a significantly higher specific surface area (SSA) and pore volume than pure GO, with values of 374 m2 g-1 and 0.36 mL g-1, respectively. The GO-CNHox-K composite also exhibited a well-developed pore structure with an SSA of 271 m2 g-1 and pore volume of 0.26 mL g-1. These findings demonstrate that Ca2+ or K+ ions effectively link GO and CNHox, validating the success of this insertion approach in reducing GO aggregation. Metal ions played a crucial role in the insertion process by facilitating electrostatic interactions and coordination bonds between GO and CNHox. This study provides new insights into reducing GO agglomeration and expanding the application of GO-based materials.

Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model

Complex structures can be understood as compositions of smaller, more basic elements. The characterization of these structures requires an analysis of their constituents and their spatial configuration. Examples can be found in systems as diverse as galaxies, alloys, living tissues, cells, and even nanoparticles. In the latter field, the most challenging examples are those of subdivided particles and particle-based materials, due to the close proximity of their constituents. The characterization of such nanostructured materials is typically conducted through the utilization of micrographs. Despite the importance of micrograph analysis, the extraction of quantitative data is often constrained. The presented effort demonstrates the morphological characterization of subdivided particles utilizing a pre-trained artificial intelligence model. The results are validated using three types of nanoparticles: nanospheres, dumbbells, and trimers. The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. These sets collate data derived from a large ensemble of specific particle domains indicating to which particle each subdomain belongs. The arrangement of subdivisions into sets to characterize complex nanoparticles expands the information gathered from microscopy analysis. The presented method, which employs a pre-trained deep learning model, outperforms traditional techniques by circumventing systemic errors and human bias. It can effectively automate the analysis of particles, thereby providing more accurate and efficient results.

Review of: "Lindemann's Change in The Structure and Structure of Nano Materials"

Review of: "Lindemann's Change in The Structure and Structure of Nano Materials"

Review of: "Investigating and Analyzing The Process of Thermal instability (Nanoparticles) in The Change Structure of The Structure and Structure of Nanomaterials"

Review of: "Investigating and Analyzing The Process of Thermal instability (Nanoparticles) in The Change Structure of The Structure and Structure of Nanomaterials"

Review of: "structure of nanomaterials, one of the thermal phenomena caused by the shrinking of the material structure is the thermal instability of nanoparticles"

Review of: "structure of nanomaterials, one of the thermal phenomena caused by the shrinking of the material structure is the thermal instability of nanoparticles"

Electrochemical and physical properties of magnesioferrite nanomaterial and photocatalytic degradation of methylene blue

This research successfully synthesized semiconductive magnesioferrite (MgFe2O4) nanomaterials using a green chemistry method that utilizes the natural extract of Moringa olefeira serving as both a reducing and oxidizing agent. The optical characteristics and crystalline structure of the MgFe2O4 nanomaterials were analysed using photoluminescence, diffuse reflectance spectroscopy, and X-ray diffraction. Additionally, Fourier transform infrared spectroscopy provided valuable insights into the chemical bonding and composition. High-resolution transmission electron microscopy was employed to obtain extensive information on crystalline size and distribution. Furthermore, the electrochemical properties were assessed through cyclic voltammetry and electrochemical impedance spectroscopy, revealing an excellent voltametric response and pseudo-capacitive behaviour associated with faradaic reactions, as well as outstanding conductivity linked to the unique charge transport mechanisms present in the MgFe2O4 structure. The effectiveness of the MgFe2O4 nanomaterials in the photodegradation of methylene blue from aqueous solutions was evaluated under visible light irradiation. Photocatalytic experiments measured the influence of various parameters, including catalyst loading, dye concentration, and pH. The MgFe2O4 nanomaterials exhibited impressive photocatalytic degradation efficiency, achieving an 81% degradation rate at pH 5.0 within 120 min. Kinetic studies indicated that the degradation process adhered to a pseudo-first-order model, with a rate constant of 0.01533 min−1, signifying a rapid reaction under optimal conditions. This study provides a thorough understanding of the electrochemical properties and enhanced photocatalytic capabilities of MgFe2O4 nanomaterials, thereby advancing green nanotechnology for environmental remediation.

How tailor-made copolymers can control the structure and properties of hybrid nanomaterials: the case of polyionic complexes.

Hybrid polyionic complexes (HPICs) are colloidal structures with a charged core rich in metal ions and a neutral hydrophilic corona. Their properties, whether as reservoirs or catalysts, depend on the accessibility and environment of the metal ions. This study demonstrates that modifying the coordination sphere of these ions can tune the properties of HPICs by altering the composition of the complexing block or varying formulation conditions. Hence, double hydrophilic block copolymers were synthesized using RAFT polymerization, with polyethylene glycol as the neutral block and different ratios of acrylic acid (AA) and vinylphosphonic acid (VPA) as the functional block and further complexed with Fe(III) ions. The resulting iron-based HPICs with higher VPA content were more stable at low pH due to stronger VPA-iron interactions, but their catalytic efficiency in the photo-Fenton process decreased at higher pH. In nanoparticle synthesis, polymers with higher VPA content produced smaller, less-defined Prussian blue nanoparticles, while a 50/50 AA/VPA ratio resulted in uniform nanoparticles and optimal reactivity. Multivariate analysis revealed that not only composition but also local structural organization impacts HPIC properties, influenced by changes in the complexing block structure (e.g., statistical, block) or formulation conditions.

Research on the Improvement of Solar Cell Performance by Low-Dimensional Semiconductor Nanomaterials

With the depletion of non-renewable resources, renewable energy sources like solar energy and hydrogen will play an important role in the future. Efficient and stable utilization of these energy sources has become a primary focus of research. Low-dimensional semiconductor nanomaterials, including quantum dots, nanowires, and thin films, exhibit unique properties due to their size being close to or smaller than certain characteristic lengths of the material. As the dimensionality decreases, quantum effects become more pronounced, leading to significant changes in the material's electronic, optical, and thermal properties. This paper focuses on the improvement of the performance of various types of solar cells through the unique properties of low-dimensional semiconductor nanomaterials while discussing the challenges in their application. This study shows that low-dimensional nanostructured semiconductor materials can enhance the performance of solar cells in energy conversion efficiency, stability and lifespan, providing new directions and methods for future solar technology development

In situ growth of three-dimensional walnut-like nanostructures of W-Ni2P@NiFe LDH/NF as efficient bifunctional electrocatalysts for water decomposition

The design of novel composite nanomaterial structures is important for the construction of advanced electrocatalysts. Nevertheless, obtaining novel electrocatalysts with excellent catalytic activity and stability is still challenging. Herein, new catalysts with a unique nanostructure of W-Ni2P@NiFe LDH/NF composed of W-doped Ni2P ultrafine nanosheets were successfully grown in situ using NiFe LDH nanostructures as the backbone support. The newly produced catalysts showed distinctive three-dimensional spherical nanostructure, beneficial to enhancing electron transport, providing abundant active sites, and promoting gas release. To increase the catalytic effectiveness, a synergy interaction was produced among W-Ni2P with NiFe LDH to yield significantly improved stability and reactivity electrocatalysts. Compared to NiFe LDH/NF and W-Ni2P/NF, the as-obtained spherically-structured W-Ni2P@NiFe LDH/NF catalysts demonstrated high catalytic efficiencies toward OER (222 mV @ 40 mA⋅cm−2), HER (195 mV @ 10 mA⋅cm−2), and total electrolysis (1.7 V @ 10 mA⋅cm−2). Besides, the catalytic activities of W-Ni2P@NiFe LDH/NF electrocatalysts compared well to most published non-precious metal catalysts and even valuable precious metal catalysts. In sum, the proposed approach to construct inexpensive, high-activity, and stable bifunctional electrocatalysts looks promising for advanced future hydrogen energy conversion applications.

Pseudomorphic Transformation in Nanostructured Thiophene-Based Materials.

This study reveals the capability of nanostructured organic materials to undergo pseudomorphic transformations, a ubiquitous phenomenon occurring in the mineral kingdom that involves the replacement of a mineral phase with a new one while retaining the original shape and volume. Specifically, it is demonstrated that the postoxidation process induced by HOF·CH3CN on preformed thiophene-based 1D nanostructures preserves their macro/microscopic morphology while remarkably altering their electro-optical properties by forming a new oxygenated phase. Experimental evidence proves that this transformation proceeds via an interface-coupled dissolution-precipitation mechanism, leading to the growth of a porous oxidized shell that varies in thickness with exposure time, enveloping the pristine smooth core. The oxygenated species exhibits stronger electron-acceptor characteristics than the core material, promoting charge transfer state formation, as confirmed by microspectroscopy and DFT calculations. This enables (i) precise modulation of the nanostructure's surface potential, allowing for the formation of entirely organic heterojunctions with precise spatial resolution via wet chemical processing; (ii) effective doping of the nanostructure, resulting in a strong change of the conductivity temperature dependence and a switch between a low and high conduction state depending on the applied bias. Overall, this work showcases an approach to engineering "impossible" composite architectures with pre-established morphology and tailored chemical-physical properties.