This work presents the generalization of the concept of universal finite-size scaling functions to continuum percolation. A high-efficiency algorithm for Monte Carlo simulations is developed to investigate, with extensive realizations, the finite-size scaling behavior of stick percolation in large-size systems. The percolation threshold of high precision is determined for isotropic widthless stick systems as $N_c{l}^2=5.63726\pm 0.00002$, with $N_c$ as the critical density and $l$ as the stick length. Simulation results indicate that by introducing a nonuniversal metric factor $A=0.106910\pm 0.000009$, the spanning probability of stick percolation on square systems with free boundary conditions falls on the same universal scaling function as that for lattice percolation.

As a finite-energy-bandgap alternative to graphene, semiconducting molybdenum disulfide (MoS₂) has recently attracted extensive interest for energy and sensor applications. In particular for broad-spectral photodetectors, multilayer MoS₂ is more appealing than its monolayer counterpart. However, little is understood regarding the physics underlying the photoresponse of multilayer MoS₂. Here, we employ scanning photocurrent microscopy to identify the nature of photocurrent generated in multilayer MoS₂ transistors. The generation and transport of photocurrent in multilayer MoS₂ are found to differ from those in other low-dimensional materials that only contribute with either photovoltaic effect (PVE) or photothermoelectric effect (PTE). In multilayer MoS₂, the PVE at the MoS₂-metal interface dominates in the accumulation regime whereas the hot-carrier-assisted PTE prevails in the depletion regime. Besides, the anomalously large Seebeck coefficient observed in multilayer MoS₂, which has also been reported by others, is caused by hot photo-excited carriers that are not in thermal equilibrium with the MoS₂ lattice.

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

Layered two-dimensional semiconductors have attracted tremendous attention owing to their demonstrated excellent transistor switching characteristics with a large ratio of on-state to off-state current, I_on/I_off. However, the depletion-mode nature of the transistors sets a limit on the thickness of the layered semiconductor films primarily determined by a given I_on/I_off as an acceptable specification. Identifying the optimum thickness range is of significance for material synthesis and device fabrication. Here, we systematically investigate the thickness-dependent switching behavior of transistors with a wide thickness range of multilayer-MoS₂ films. A difference in I_on/I_off by several orders of magnitude is observed when the film thickness, t, approaches a critical depletion width. The decrease in I_on/I_off is exponential for t between 20 nm and 100 nm, by a factor of 10 for each additional 10 nm. For t larger than 100 nm, I_on/I_off approaches unity. Simulation using technical computer-aided tools established for silicon technology faithfully reproduces the experimentally determined scaling behavior of I_on/I_off with t. This excellent agreement confirms that multilayer-MoS₂ films can be approximated as a homogeneous semiconductor with high surface conductivity that tends to deteriorate I_on/I_off. Our findings are helpful in guiding material synthesis and designing advanced field-effect transistors based on the layered semiconductors.

On the basis of Monte Carlo simulations, the present work systematically investigates how conductivity exponents depend on the ratio of stick-stick junction resistance to stick resistance for two-dimensional stick percolation. Simulation results suggest that the critical conductivity exponent extracted from size-dependent conductivities of systems exactly at the percolation threshold is independent of the resistance ratio and has a constant value of 1.280\ifmmode\pm\else\textpm\fi{}0.014. In contrast, the apparent conductivity exponent extracted from density-dependent conductivities of systems well above the percolation threshold monotonically varies with the resistance ratio, following an error function, and lies in the vicinity of the critical exponent.

The formation of ultrathin silicide films of ${\text{Ni}}_{1-\text{x}}{\text{Pt}}_{\text{x}}$ at $450–850°\text{C}$ is reported. Without Pt $\left(\text{x}=0\right)$ and for $t_{\text{Ni}}<4\text{nm}$ , epitaxially aligned ${\text{NiSi}}_{2-\text{y}}$ films readily grow and exhibit extraordinary morphological stability up to $800°\text{C}$ . For $t_{\text{Ni}}\ge 4\text{nm}$ , polycrystalline NiSi films form and agglomerate at lower temperatures for thinner films. Without Ni $\left(\text{x}=1\right)$ and for $t_{\text{Pt}}=1–20\text{nm}$ , the annealing behavior of the resulting PtSi films follows that for the NiSi films. The results for ${\text{Ni}}_{1-\text{x}}{\text{Pt}}_{\text{x}}$ of other compositions support the above observations. Surface energy is discussed as the cause responsible for the distinct behavior in phase formation and morphological stability.