Materials Today Quantum最新文献

This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability.

Carbon quantum dots (CQDs) are widely used in optical biosensors due to their good biocompatibility and easy synthesis type. Although the carbon sources for preparing CQDs are quite extensive, it is not common to prepare CQDs using herbs as carbon sources. Therefore, CQDs for fluorescence determination of Fe³⁺ and dopamine (DA) were prepared by microwave heating using senna leaf as carbon source. The prepared CQDs showed good dispersion and uniform sphericity under transmission electron microscopy (TEM), with an average particle size of 3.510 nm. Under ultraviolet light, CQDs fluoresce brightly blue and have a strong fluorescence (>1.200*10³ a.u.), with no change in fluorescence intensity over a week. The prepared CQDs were quenched by Fe³⁺ and DA probably due to the static burst effect, which can be confirmed by X-ray photoelectron spectroscopy (XPS) and fourier-transform infrared spectroscopy (FT-IR) analyses. The method has a good linear relationship for Fe³⁺ in the range of 10–3000 μmol/L with a determination limit of 0.1671 μmol/L, and an excellent linear relationship for DA in the range of 5–3000 μmol/L with a determination limit of 0.1653 μmol/L. The method was applied to the determination of Fe³⁺ and DA in real samples, and the recovery rate was satisfactory.

Quantum dots are semiconductor nanoparticles where electrons’ motion is confined within the three physical dimensions of the nanoparticle, such that discretization of energy levels is observed. In this article, quantum dots of Bi₂Te₃, with sizes around 9 ± 2 nm and energy bandgap around ∼ 2.8 eV, were successfully synthesized by pulsed laser ablation in liquids. Those dots were found to be within the strong confinement regime.

Multiple-$Q$ states as represented by a magnetic skyrmion crystal and hedgehog crystal have been extensively studied in recent years owing to their unconventional physical properties. The materials hosting multiple-$Q$ states have been so far observed in a variety of lattice structures and chemical compositions, which indicates rich stabilization mechanisms inducing the multiple-$Q$ states. We review recent developments in the research of the stabilization mechanisms of such multiple-$Q$ states with an emphasis on the microscopic spin interactions in momentum space. We show that an effective momentum-resolved spin model is a canonical model for not only understanding the microscopic origin of various multiple-$Q$ states but also exploring further exotic multiple-$Q$ states with topological properties. We introduce several key ingredients to realize the magnetic skyrmion crystal with the skyrmion numbers of one and two, hedgehog crystal, meron–antimeron crystal, bubble crystal, and other multiple-$Q$ states. We also review that the effective spin model can be used to reproduce the magnetic phase diagram in experiments efficiently.

Since the accidental discovery of carbon quantum dots (CQDs) in 2004, they have been widely used in the field of fluorescence sensing by combining their good optical and physicochemical properties with a wide source of raw materials and a simple synthesis process. In this work, we have synthesised sulphur-doped carbon quantum dots L-CyS/AA CQDs by a one-step microwave method using L-cysteine (L-CyS) and ascorbic acid (AA) as carbon and sulphur sources. It was also analysed by fluorescence spectroscopy, X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM). The prepared L-CyS/AA CQDs showed good dispersion under TEM with a spherical shape and an average particle size of 7.3 nm. L-CyS/AA CQDs were observed to be bright blue-green fluorescent with strong fluorescence (>3.5*10³ a.u.) under UV light irradiation. PASS 0 logic gate operation can be achieved by controlling different fluorescent input and output. L-CyS/AA CQDs were able to achieve selective detection of Co²⁺ with a LOD of 63.2 μM, which provides a new method for Co²⁺ detection that can be used for the detection of Co²⁺ in real water samples.

Recent findings regarding spin-orbit torques (SOTs) and current-induced magnetization switching in ferromagnetic (FM) single layers have attracted substantial attention due to the advantage of not necessitating the use of heavy-metal layers. Nevertheless, despite prior studies on the interior structural engineering of the SOT, the external techniques for manipulating the SOT in the FM single layer remains elusive, which is indispensable for the practical application of the single layer SOT devices. Here, we demonstrate external manipulation of SOT generation in CoPd single layer through the fabrication of CoPd film with a composition gradient, utilizing the H₂-absorption property of Pd. It is found that the H-induced strain within the CoPd film plays a pivotal role in generating SOT. Meanwhile, we demonstrate that the critical current density required for the current-induced magnetization switching is markedly diminished with the application of H₂ due to the enhanced SOT generation and reduced perpendicular magnetic anisotropy energy. Our findings offer a straightforward method for external manipulation of single layer SOT devices, and hold the potential for applications of the spintronic devices.

This work used a variational quantum circuit (VQC) in conjunction with a quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as corrosion inhibitors. Compared to conventional methods like multilayer perceptron neural networks (MLPNN), the VQC model predicts the CIE more accurately. With a coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.989, 0.027, 0.024, and 0.019, respectively, VQC performs better. The established VQC model predicts the CIE with outstanding predictive accuracy for four newly synthesized pyrimidine derivative compounds: 1-(4-fluorophenyl)- 3-(4-(pyridin-4-ylmethyl)phenyl)urea (P1), 1-phenyl-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P2), 1-(4-methylphenyl)-3-(4-(pyridin-4-ylmethyl)phenyl)urea (P3), and quaternary ammonium salt dimer (P4). It generates remarkably high CIE values of 92.87, 94.05, 94.96, and 96.93 for P1, P2, P3, and P4, respectively. With its ability to streamline the testing and production processes for novel anti-corrosion materials, this innovative approach holds the potential to revolutionize the market.