RESUMEN
Machine learning is playing a crucial role in optimizing material synthesis, particularly in scenarios where several parameters related to growth exhibit different and significant outcomes. An example of such complexity is the growth of atomically thin semiconductors through chemical vapor deposition (CVD), where multiple parameters can influence the thermodynamics and reaction kinetics involved in the synthesis. Herein, we performed a set of orthogonal experiments, varying the key parameters such as temperature, carries gas flux and precursor position to identify the optimal conditions for maximizing covered area and the size of rhenium disulfide (ReS2) crystals. The experimental results were used to establish correlations among the three thermodynamic variables through an artificial neural network. Contour plots were then generated to visualize the impact on the coverage and flake size of the crystals. This study demonstrates the capability of machine learning to enhance the potential of CVD-growth for the integration of 2D semiconductors like ReS2at larger scales.
RESUMEN
Atomically thin two-dimensional (2D) materials have gained significant attention from the research community in the fabrication of high-performance optoelectronic devices. Even though there are various techniques to improve the responsivity of the photodetector, the key factor limiting the performance of the photodetectors is constrained photodetection spectral range in the electromagnetic spectrum. In this work, a mixed-dimensional 0D/2D SnS2-QDs/monolayer MoS2 hybrid is fabricated for high-performance and broadband (UV-visible-near-infrared (NIR)) photodetector. Monolayer MoS2 is deposited on SiO2/Si using chemical vapor deposition (CVD), and SnS2-QDs are prepared using a low-cost solution-processing method. The high performance of the fabricated 0D/2D photodetector is ascribed to the band bending and built-in potential created at the junction of SnS2-QDs and MoS2, which enhances the injection and separation efficiency of the photoexcited charge carriers. The mixed-dimensional structure also suppresses the dark current of the photodetector. The decorated SnS2-QDs on monolayer MoS2 not only improve the performance of the device but also extends the spectral range to the UV region. Photoresponsivity of the device for UV, visible, and NIR region is found to be â¼278, â¼ 435, and â¼189 A/W, respectively. Fabricated devices showed maximum responsivity under the visible region attributed to the high absorbance of monolayer MoS2. The response time of the fabricated device is measured as â¼100 ms. These results reveal that the development of a mixed-dimensional (0D/2D) SnS2-QDs/MoS2-based high-performance and broadband photodetector is technologically promising for next-generation optoelectronic applications.
RESUMEN
Chitosan-gold nanoparticle (CS/AuNP) thin films were synthesized through the chemical reduction of HAuCl4 in sodium citrate/chitosan solutions. The dielectric and dynamic mechanical behaviors of CS/AuNP films have been investigated as a function of moisture and HAuCl4 content. Two relaxation processes in the nanocomposites have been observed. The α-relaxation process is related to a glass transition in wet CS/AuNP films. However, in dry composites (with 0.2 wt% of moisture content), the glass transition vanished. A second relaxation process was observed from 70 °C to the onset of thermal degradation (160 °C) in wet films and from 33 °C to the onset of degradation in dry films. This relaxation is identified as the σ-relaxation and may be related to the local diffusion process of ions between high potential barriers in disordered systems. The α- and σ-relaxation processes are affected by the HAuCl4 content of the solutions from which films were obtained because of the interaction between CS, sodium succinate, and gold nanoparticles. With about 0.6 mM of HAuCl4, the conductivity of both wet and dry films sharply increased by six orders, corresponding to the percolation effect, which may be related to the appearance of a conductivity pathway between AuNPs, HAuCl4, and NaCl.