Applied Physics A最新文献

In recent years, many optical industries have shown interest in employing diamond-like carbon (DLC) coatings in their products. DLC layers were successfully deposited using a low frequency pulsed magnetron sputtering system on glass substrates at room temperature. Post-thermal treatments were performed on the coated substrates at various temperatures of 150 C, 200 C and 250 C. Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) revealed significant structural changes in the DLC layers as a result of the thermal post-treatment process. Additionally, the morphology of the layers was analyzed using field emission scanning electron microscopy (FESEM). Furthermore, UV-visible spectroscopy was utilized to investigate the optical properties of the films. The optical parameters of the deposited DLC layers were subsequently calculated. The results demonstrated that the optical properties of the coated DLC films could be effectively tuned through post-thermal treatment at relatively low temperatures.

This work explores the connection between neural networks and cloaking systems. Specifically, it focuses on achieving bifunctional cloaking of thermal and electric fields using the wave equation and heat conduction equation. The parameters of neural networks, including the activation function, the bias function, and the weight functions, are incorporated to enhance the design of the bifunctional cloaking mechanism. The primary aim is to address various practical challenges, such as cloaking and shielding, by offering a novel approach to simplify and optimize bifunctional cloaking. A significant advantage of this principle is its ability to elucidate bifunctional cloaking for thermal and electric fields more effectively and efficiently than existing methods. To the best of our knowledge, this is the first approach to comprehensively address these challenges.

The semiconductor industry is transitioning from two-dimensional (2D) to three-dimensional (3D) structures to enhance integrated circuits’ performance and density. In this study, a dual-beam laser system combining femtosecond (Fs) and continuous wave (CW) lasers was used to efficiently recrystallize amorphous silicon into high-quality polycrystalline silicon (poly-Si). The Fs laser applies localized heat in short bursts to avoid thermal damage, while the CW laser provides sustained energy to facilitate recrystallization. Experimental results and COMSOL simulations demonstrate the dual-beam system’s effectiveness in producing poly-Si with minimal thermal impact on adjacent components. Raman spectroscopy confirms the quality of the poly-Si, showing a peak close to single-crystal silicon (518.0 cm^− 1). A fabricated photodetector using nanostructured poly-Si exhibited responsivity comparable to commercial silicon photodiodes, highlighting the practical applications of our method. The dual-beam laser system offers a promising solution for future semiconductor technologies by minimizing thermal damage and ensuring effective recrystallization.

We present a theoretical investigation aimed at understanding how external electric fields influence resonant tunneling and quantum transport in inverse parabolic multibarrier semiconductor heterostructures. The main problem addressed is the lack of comprehensive studies describing field-induced localization and miniband modulation in smoothly varying potential profiles. The analysis is carried out using the non-equilibrium Green’s function formalism with the finite element method, which allows accurate determination of transmission spectra, resonant energy levels, and current density-voltage characteristics. Our results highlight the strong dependence of resonant tunneling features on the structural parameters of the system, including the number of barriers, as well as the width of wells and height of barriers. It is found that increasing the number of barriers enhances the complexity of the transmission spectrum, leading to sharper resonant peaks and modified miniband formation. Furthermore, the application of an external electric field introduces a substantial shift in the resonant energy levels and significantly alters the transmission probability. Numerical results indicate that for a field-free structure, unity transmission occurs at specific resonance energies (E (:approx:) 20–250 meV for N_B = 2 and 5), while under a high electric field (F = 50 kV/cm), the transmission significantly decreases and resonance peaks vanish due to wave localization. The calculated current–voltage characteristics reveal a pronounced negative differential resistance behavior. As the barrier height increases from 250 meV to 500 meV, the NDR region broadens while the peak-to-valley current ratio decreases. These findings emphasize the tunability of inverse parabolic multibarrier structures and their potential applications in high-frequency nanoelectronic and quantum device technologies.

Graphene and III–V semiconductor heterostructures have emerged as promising materials for high-performance photodetectors due to their broadband absorption, ultrafast carrier dynamics, and compatibility with advanced nanophotonic architectures. Leveraging these material strengths, in this work we have designed and analyzed a plasmonic metasurface-integrated graphene/InGaAs heterojunction photodetector to exploit material-driven light–matter interactions. Using COMSOL Multiphysics^®, the optical and electrical responses are systematically analyzed across the C + L band. The proposed device achieves a peak responsivity of 0.82 A/W, external quantum efficiency above 90%, and a detectivity of ~ 1.3 × 10¹² Jones at zero bias, supporting self-powered operation. in addition, we demonstrate a 3-dB bandwidth greater than 60 GHz and energy consumption less than 0.01 mW (< 1 fJ/bit), representing a major advancement compared to regular graphene/InGaAs structures. The increase in performance comes from the field confinement from plasmon resonance produced by metallic nanodisk metasurfaces, which significantly increase absorption and carrier transport inside the heterostructure. Taken together, these results validate the promise of a metasurface engineered graphene/InGaAs platform for next-generation optoelectronic and integrated photonic devices.

The resonance frequency of the Fe/MgO system has been modeled by investigating the influence of processing parameters, including MgO nanoparticle size (10–1000 nm), pressing pressure (600–1250 MPa), sintering temperature (0–900 °C), sintering time (0–60 min), and annealing atmosphere (air, nitrogen, or no annealing). The resonance frequency (0.9–3300 kHz) was successfully predicted using machine learning (Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and random forest (RF)) models. The models achieved an R-squared (R²) of 0.99, thereby explaining 99% of the variance. The predictive accuracy of the models was further assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). High R² values and consistently low RMSE and MAE scores demonstrate the robustness of the models for modeling resonance frequency. This work highlights key factors for optimizing the resonance frequency of Fe/MgO systems. SHAP values and feature importance analyses identified time and temperature as the most influential parameters across all models. The notable impacts of time and temperature indicate that optimizing the sintering process can lead to significant enhancements in material performance. The critical role of sintering parameters in optimizing the resonance properties of Fe/MgO systems, paving the way for improved material performance in high-frequency magnetic applications.

Applying green synthesis methods to produce nanoparticles is a crucial strategy to minimise the negative effects sometimes associated with traditional processes. Gold nanoparticles (AuNPs)s were synthesised using Annona muricata fruit extract. The nanoparticles were further examined using several analytical methods, including UV–Vis, FTIR spectroscopy, FE-SEM, TEM, XRD, and zeta potential. They were analyzed for their antioxidant and antidiabetic characteristics. The effective synthesis of the AuNPs was visually confirmed by a gradual change in colour, starting from a pale yellow to a deep ruby red hue. The UV–Vis spectra exhibited a distinct peak at 530 nm, confirming the initial validation of the biosynthesised AuNPs. The FTIR spectrum was utilized to identify various functional groups that could potentially participate in the synthesis, Stabilisation, and capping of AuNPs. The FE-SEM image revealed the presence of AuNPs exhibiting a combination of spherical and triangular morphologies, with dimensions ranging from 30 to 40 nm without aggregation. TEM confirmed the spherical morphology, while XRD analysis highlighted the crystalline structure, and zeta potential confirmed the stability of the particles. DPPH, total antioxidant and ABTS methods were used to measure the antioxidant properties of A. muricata, AuNPs, and gallic acid, respectively. AuNPs showed higher free radical scavenging activity IC₅₀ values of 25 µg/mL, 22 µg/mL, and 20 µg/mL compared to 30 µg/mL, 28 µg/mL, 25 µg/mL for Gallic acid and 45 µg/mL, 40 µg/mL, 48 µg/mL for A. muricata aqueous extract, respectively. Nanoparticles exhibited α-glucosidase strong inhibition of IC₅₀ of 43 μg/mL and α-amylase inhibition with IC₅₀ of 36 μg/mL, compared to fruit extracts. AuNPs synthesised by green methods exhibited the strongest antioxidant and antidiabetic properties compared to a plant extract. AuNPs prepared using A. muricata can be enhanced by modifying their surfaces with targeting molecules, thereby improving their precision in delivering drugs to diseased cells and increasing their therapeutic efficacy, which paves the way towards safer and targeted nanotherapies for chronic diseases.

In this study, the effect of thermal annealing on the optical and non-linear optical properties of MoS₂ quantum dots decorated graphene oxide (MoS₂QDs/GO) has been reported. The MoS₂ quantum dots (MoS₂QDs) and graphene oxide were separately synthesized by chemical exfoliation method followed by decoration of MoS₂QDs on graphene oxide (GO) thin film by dip coating method. In the next step, the MoS₂QDs/GO samples were annealed at temperature 300 °C, 500 °C and 600 °C in an ambient environment. The structural and morphological studies were carried out using FESEM, TEM and AFM techniques. The homogeneous spreading of MoS₂QDs on large surface area GO nanosheets was confirmed from FESEM and TEM images. AFM images reveal the thickness of MoS₂QDs/GO are in the range of nanometers. The optical absorption measurement done using UV visible spectroscopy has revealed a gradual shift of band edge with increasing annealing temperature. The vibrational properties of GO and MoS₂QDs/GO samples were studied using Raman spectroscopic measurements. More importantly Z scan measurements exhibit enhanced nonlinear optical (NLO) performance in annealed samples. This enhancement is attributed to increased π–π conjugation and stronger electronic coupling between MoS₂QDs and GO after annealing. Our study highlights the potential of annealed MoS₂QDs/GO nanocomposites for advanced optical limiters, laser protection systems, and next-generation photonic devices.

Understanding transient ion dynamics in electrolyte-gated transistors (EGTs) is essential for predicting their electrical behavior. Ions respond slower than charge carriers to electric fields, sometimes causing transient currents that disrupt steady-state operation. Although prior studies focused on transient current and transient time, achieving steady-state conditions during transfer and output curve measurements remains challenging, indicating gaps in the understanding of ion-charge carrier interactions. This work investigates electrical characterization rather than device structure, revealing how scan rate significantly influences transfer curves in organic electrochemical transistors (OECTs), with transient currents impacting its performance. To do it, we fabricated well-established EGTs based on the semiconducting polymer poly(3-hexylthiophene-2,5diyl) (P3HT), focusing on understanding the fundamental processes that occur in device operation rather than optimizing materials or device structure. We also highlight the importance of reporting measurement history, since increasing and decreasing scan rate sequences yield asymmetric results due to retention effects, likely from ion doping. The transient time ((tau)) was analyzed under square-wave gate voltages, showing to depend also on gate bias, whether into accumulation or transition between accumulation and depletion regimes. These findings demonstrate that EGT’s performance is influenced by charge transport regime transitions, scan rates, and prior measurements. Notably, a retention effect suggests that performing a transfer curve at a low scan rate induces permanent changes, which can then be leveraged in subsequent OECT’s measurements to achieve faster response time. This study provides new insights into optimizing EGT’s operation through controlled ion dynamics.

The primary objective of this study is to demonstrate the feasibility of employing Laser-Induced Breakdown Spectroscopy (LIBS) in combination with machine learning algorithms for the identification and assessment of abiotic stress in plants. The machine learning approaches considered are multilinear regression (MLR), support vector regression (SVR), partial least squares regression (PLSR), least absolute shrinkage and selection operator (LASSO), and Gaussian process regression (GPR). The stress condition (based on nutrient content - Ca and K) in the sample is also estimated using the calibration-free LIBS (CF-LIBS) method. The experiments are carried out by varying the laser irradiances and stand-off plasma collection distances. It is observed that the Ca concentration in the normal sample is higher than the K concentration, regardless of the incident laser irradiance and stand-off plasma collection distance. The opposite trend is observed in abiotically stressed samples. A clear distinction is observed between normal and abiotically stressed samples in the LIBS spectrum. The GPR method trained on normalized dataset ((dfrac{I_{lambda }}{I_{656 nm}})) resulted in achieving better estimate of Ca and K in normal (R(^{varvec{2}}) = 0.94, Ca = 47891 (varvec{pm }) 1895 ppm; R(^{varvec{2}}) = 0.90, K= 21234 (varvec{pm })2147 ppm) and abiotic stressed samples (R(^{varvec{2}}) = 0.92, Ca = 38715 (varvec{pm }) 1985 ppm; R(^{varvec{2}}) = 0.91, K = 57857 (varvec{pm }) 2458 ppm). The CF-LIBS based Ca and K estimate (SoD= 0.5 m, irradiance = 2.5x10(^{varvec{10}}) W/cm(^{varvec{2}})) is in agreement with GPR method trained on normalised dataset, and atomic emission spectroscopy (AES) measurements. The normalized LIBS dataset yielded zero misclassification when evaluated using the Random Forest classifier. The study concluded that the normalized LIBS–GPR model can be adapted for real-time operation following calibration of SoD and laser irradiance.