Graphene Quantum Dots: Novel Properties and Their Applications for Energy Storage Devices

Fabrication of Co-Sb Junction Nanowires by Galvanostatic Electrodeposition

This work presents the synthesis of CoSb3 one-dimensional (1D) thermoelectric nanomaterials using electrodeposition under galvanostatic conditions and polycarbonate membranes as a template (50 nm diameter pores). Cyclic voltammetry measurements have been performed to get preliminary information on the electrochemical reduction process of the involved species. Different current density values in the range 1-4 mA cm-2 have been applied, leading to the formation of nanowires (NWs) and micro- and nanomushroom caps, as evidenced by the scanning electron microscopy and scanning transmission electron microscopy investigations. Through fine-tuning of the current density the desired Co/Sb atomic ratio could be achieved. Energy-dispersive X-ray spectroscopy analysis showed the formation of CoSb3 at 1.4 mA cm-2, and it has also been confirmed by high-resolution transmission electron microscopy and micro-Raman spectroscopy. In this work, we present for the first time the fabrication of a CoSb3-CoxSby heterojunction on the same NW exhibiting Sb-rich and Co-rich alloy segments, prepared by electrodeposition from the same electrolyte by simply varying the applied current density.

Atomic Layer Deposition-A Versatile Toolbox for Designing/Engineering Electrodes for Advanced Supercapacitors

Atomic layer deposition (ALD) has become the most widely used thin-film deposition technique in various fields due to its unique advantages, such as self-terminating growth, precise thickness control, and excellent deposition quality. In the energy storage domain, ALD has shown great potential for supercapacitors (SCs) by enabling the construction and surface engineering of novel electrode materials. This review aims to present a comprehensive outlook on the development, achievements, and design of advanced electrodes involving the application of ALD for realizing high-performance SCs to date, as organized in several sections of this paper. Specifically, this review focuses on understanding the influence of ALD parameters on the electrochemical performance and discusses the ALD of nanostructured electrochemically active electrode materials on various templates for SCs. It examines the influence of ALD parameters on electrochemical performance and highlights ALD's role in passivating electrodes and creating 3D nanoarchitectures. The relationship between synthesis procedures and SC properties is analyzed to guide future research in preparing materials for various applications. Finally, it is concluded by suggesting the directions and scope of future research and development to further leverage the unique advantages of ALD for fabricating new materials and harness the unexplored opportunities in the fabrication of advanced-generation SCs.

Recent Advances of Graphene Quantum Dots in Chemiresistive Gas Sensors

Graphene quantum dots (GQDs), as 0D graphene nanomaterials, have aroused increasing interest in chemiresistive gas sensors owing to their remarkable physicochemical properties and tunable electronic structures. Research on GQDs has been booming over the past decades, and a number of excellent review articles have been provided on various other sensing principles of GQDs, such as fluorescence-based ion-sensing, bio-sensing, bio-imaging, and electrochemical, photoelectrochemical, and electrochemiluminescence sensing, and therapeutic, energy and catalysis applications. However, so far, there is no single review article on the application of GQDs in the field of chemiresistive gas sensing. This is our primary inspiration for writing this review, with a focus on the chemiresistive gas sensors reported using GQD-based composites. In this review, the various synthesized strategies of GQDs and its composites, gas sensing enhancement mechanisms, and the resulting sensing characteristics are presented. Finally, the current challenges and future prospects of GQDs in the abovementioned application filed have been discussed for the more rational design of advanced GQDs-based gas-sensing materials and innovative gas sensors with novel functionalities.

Photothermal therapy using graphene quantum dots

The rapid development of powerful anti-oncology medicines have been possible because of advances in nanomedicine. Photothermal therapy (PTT) is a type of treatment wherein nanomaterials absorb the laser energy and convert it into localized heat, thereby causing apoptosis and tumor eradication. PTT is more precise, less hazardous, and easy-to-control in comparison to other interventions such as chemotherapy, photodynamic therapy, and radiation therapy. Over the past decade, various nanomaterials for PTT applications have been reviewed; however, a comprehensive study of graphene quantum dots (GQDs) has been scantly reported. GQDs have received huge attention in healthcare technologies owing to their various excellent properties, such as high water solubility, chemical stability, good biocompatibility, and low toxicity. Motivated by the fascinating scientific discoveries and promising contributions of GQDs to the field of biomedicine, we present a comprehensive overview of recent progress in GQDs for PTT. This review summarizes the properties and synthesis strategies of GQDs including top-down and bottom-up approaches followed by their applications in PTT (alone and in combination with other treatment modalities such as chemotherapy, photodynamic therapy, immunotherapy, and radiotherapy). Furthermore, we also focus on the systematic study of in vitro and in vivo toxicities of GQDs triggered by PTT. Moreover, an overview of PTT along with the synergetic application used with GQDs for tumor eradication are discussed in detail. Finally, directions, possibilities, and limitations are described to encourage more research, which will lead to new treatments and better health care and bring people closer to the peak of human well-being.

Recent advancement in quantum dot-based materials for energy storage applications: a review

The need for energy storage and conversion is growing as a result of the worsening consequences of climate change and the depletion of fossil fuels. Energy conversion and storage requirements are rising as a result of environmental problems including global warming and the depletion of fossil fuels. The key to resolving the energy crisis is anticipated to be the quick growth of sustainable energy sources including solar energy, wind energy, and hydrogen energy. In this review, we have focused on discussing various quantum dots (QDs) and polymers or nanocomposites used for SCs and have provided examples of each type's performance. Effective QD use has really led to increased performance efficiency in SCs. The use of quantum dots in energy storage devices, batteries, and various quantum dots synthesis have all been emphasized in a number of great literature articles. In this review, we have homed in on the electrode materials based on quantum dots and their composites for storage and quantum dot based flexible devices that have been published up to this point.

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models' predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand-brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

A DFT Study of Ruthenium fcc Nano-Dots: Size-Dependent Induced Magnetic Moments

Many areas of electronics, engineering and manufacturing rely on ferromagnetic materials, including iron, nickel and cobalt. Very few other materials have an innate magnetic moment rather than induced magnetic properties, which are more common. However, in a previous study of ruthenium nanoparticles, the smallest nano-dots showed significant magnetic moments. Furthermore, ruthenium nanoparticles with a face-centred cubic (fcc) packing structure exhibit high catalytic activity towards several reactions and such catalysts are of special interest for the electrocatalytic production of hydrogen. Previous calculations have shown that the energy per atom resembles that of the bulk energy per atom when the surface-to-bulk ratio < 1, but in its smallest form, nano-dots exhibit a range of other properties. Therefore, in this study, we have carried out calculations based on the density functional theory (DFT) with long-range dispersion corrections DFT-D3 and DFT-D3-(BJ) to systematically investigate the magnetic moments of two different morphologies and various sizes of Ru nano-dots in the fcc phase. To confirm the results obtained by the plane-wave DFT methodologies, additional atom-centred DFT calculations were carried out on the smallest nano-dots to establish accurate spin-splitting energetics. Surprisingly, we found that in most cases, the high spin electronic structures had the most favourable energies and were hence the most stable.

Nanomaterials for Catalysis and Energy Storage

The development of nanomaterials with different shapes and sizes and which are utilized as effective materials for energy and environmental applications constitutes a challenge for researchers [...].