Electric Tunnel Research Articles

Pseudospin-filter tunneling of massless Dirac fermions

Abstract The tunneling of the massless Dirac fermions through a vector potential barrier are theoretically investigated, where the vector potential can be introduced by the very high and very thin (δ-function) magnetic potential barriers. We show that, distinct from the previously studied electric barrier tunneling, the vector potential barriers are more transparent for pseudospin-1/2 Dirac fermions but more obstructive for pseudospin-1 Dirac fermions. By tuning the height of the vector potential barrier, the pseudospin-1/2 Dirac fermions remain transmitted, whereas the transmission of the pseudospin-1 Dirac fermions is forbidden, leading to a pseudospin filtering effect for massless Dirac fermions.

Surface texture image classification of carbon/phenolic composites in extreme environments using deep learning

AbstractThe classification of ablation images holds significant practical value in thermal protection structures, as it enables the assessment of heat and corrosion resistance of composites. This paper proposes an image‐based deep learning framework to identify the surface texture of carbon/phenolic composites ablative images. First, ablation experiments and collection of surface texture images of carbon/phenolic composites under different thermal environments were conducted in an electric arc wind tunnel. Then, a deep learning model based on a convolutional neural network (CNN) is developed for ablative image classification. The pre‐trained network is ultimately employed as the input for transfer learning. The network's feature extraction layer is trained using the ImageNet dataset, while the global average pooling addresses specific classification tasks. The test results demonstrate that the proposed method effectively classifies the relatively small surface texture dataset, enhances the classification performance of ablative surface texture with an accuracy of up to 97.6%, and exhibits robustness and generalization capabilities.Highlights The paper proposes a new deep learning classification method for ablative images. A model highly sensitive to small and weak features is built. Transfer learning and data enhancement techniques are introduced into classification.

Assessment of effective electrical conductivity in spherocylindrical carbon nanotube composites incorporating intrinsic properties of CNTs and interphase layer

This study introduces an analytical model using mean-field theory to estimate the effective electrical conductivity in composites of spherocylindrical carbon nanotubes (CNTs). The model adjusts the interphase layer's thickness and conductivity based on CNT concentration, affecting the electrical tunneling effect, and incorporates the properties of both CNTs and the interphase layer. Validated through detailed comparisons with experimental data, the model accurately predicts the electrical conductivities of polymer-CNT composites (PCNTs). Mean Squared Error analysis highlights its enhanced precision and superiority over traditional cylindrical CNT models. The model also explores how CNT dimensions, intrinsic conductivity, tunneling distance, potential barrier height, and variations in interphase thickness and conductivity influence electrical conductivity and percolation thresholds. This analytical model improves accuracy in predicting PCNT electrical conductivity and provides critical insights for designing and optimizing conductive polymer nanocomposites.

Highly radiation-stable DLC coatings for a new class of detectors: Structural and morphological features

Within the framework of the FTM-NEXT INFN (Fast Time Micropattern gaseous detectors - next of Nuclear Physics National Institute) experiment, we produced hydrogen-free diamond-like carbon films through pulsed-laser deposition to serve as resistive layers in modern resistive micro-pattern gaseous detectors that must work in extreme radiation environments at future colliders. To obtain homogeneous diamond-like carbon coatings, over medium-to-large size (3 cm × 3 cm), with excellent adhesion to the substrate and with typical surface resistivity values in the range of 1–100 MOhm/sq, growth conditions had to be optimized. In this paper we report on the stability of resistive diamond-like carbon layers subjected to increasing doses of irradiation with proton beams accelerated to an energy of 2 MeV. The morphological, structural, and electrical properties, also at the nanoscale level, of diamond-like carbon coatings following ion irradiation were studied by electron microscopy, electron diffraction, electrical transport characterization and scanning tunneling spectroscopy.

Robustness of Raised Buried Oxide Ferro Electric Tunnel FET in presence of Temperature and Traps and its Analog/RF Performance

Robustness of Raised Buried Oxide Ferro Electric Tunnel FET in presence of Temperature and Traps and its Analog/RF Performance

Numerical study on smoke temperature and exhaust efficiency in electric cable tunnel

Cable tunnels are the lifeline of cities. Once a serious fire occurs on cables, the toxic smoke generated by cable combustion spreads and escapes, causing serious harm to the surrounding environment and personnel. The heat generated can also ignite nearby combustibles, causing casualties and property damage. Based on the above issues, using fluid dynamics methods, fire dynamic simulation was used to simulate cable tunnel fire scenarios under four variables: wind speed, fire source power, smoke outlet height, and smoke exhaust airflow. The temperature distribution law of the ceiling smoke layer inside the cable tunnel, smoke generation law, tunnel smoke exhaust efficiency, and other factors were clarified, revealing the impact mechanism of the above factors on tunnel fire smoke exhaust efficiency and smoke performance improvement methods. The research results indicate that the combination mode of exhaust air volume and ventilation air speed can effectively control tunnel fire smoke, providing a reference for the fire prevention design of cables in urban cable tunnels.

Aerosol assisted synthesis of a pH responsive curcumin anticancer drug nanocarrier using chitosan and alginate natural polymers

In recent years, several nanocarrier synthesis methods have been developed. In cancer therapy, the use of smart nanocarriers is of interest. Smart nanocarriers respond to their environment and can release their cargo in a controlled manner under the action of internal or external stimuli. In this work, we report on the development of an aerosol-assisted method for the synthesis of curcumin-loaded chitosan/alginate-based polymeric nanocarrier (CurNCs). A custom-fabricated multi-nebulizer system was utilized for the synthesis of CurNCs. The developed system comprises three main parts a sprayer, an electric heater tunnel, and a collector. Curcumin and chitosan solutions were sprayed using a pneumatic multinebulizer into the electric heater tunnel to form chitosan-curcumin assemblies. Then, the aerosol was guided into the collector solution containing sodium alginate and tri-poly phosphate aqueous solution for further cross-linkage. The synthesized CurNCs were characterized using TEM, DLS, and FTIR techniques. The TEM size of the nanoparticles was 8.62 ± 2.25 nm. The release experiments revealed that the nanocarrier is sensitive to the environment pH as more curcumin is released at acidic pH values (as is the case for cancerous tissues) compared to physiological pH. The curcumin content of the nanocarrier was 77.27 mg g−1 with a drug loading efficiency of 62%. The in-vitro cytotoxicity of the synthesized nanocarrier was evaluated against the MCF7 breast cancer cell line. The IC50 concentrations for CurNCs and curcumin were obtained as 14.86 and 16.45 mg mL−1, respectively. The results showed that while the empty nanocarrier shows non-significant cytotoxicity, the CurNCs impact the cell culture and cause prolonged cell deaths. Overall, pH-responsive curcumin polymeric nanocarrier was synthesized using a custom fabricated aerosol-based method. The method enabled fast and feasible synthesis of the nanocarrier with high efficiency.

Machine Learning‐Aided Data Analysis in Single‐Protein Conductance Measurement with Electron Tunneling Probes†

Comprehensive SummaryThe electrical tunneling sensors have excellent potential in the next generation of single‐molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities. Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information. Despite the extraordinarily large amount of data that has been gathered, it is still difficult to correlate signal transformations with molecular processes, which creates great obstacles for signal analysis. Machine learning is an effective tool for data analysis that is currently gaining more significance. It has demonstrated promising results when used to analyze data from single‐molecule electrical measurements. In order to extract meaningful information from raw measurement data, we have combined intelligent machine learning with tunneling electrical signals. For the purpose of analyzing tunneling electrical signals, we investigated the clustering approach, which is a classic algorithm in machine learning. A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering. Additionally, customized statistical algorithms were designed. It has been proven to efficiently gather molecular information and enhance the effectiveness of data analysis.

Deep learning inversion method of tunnel resistivity synthetic data based on modelling data

AbstractDifferent water‐bearing geological structures in front of the tunnel face are the main cause of tunnel water inrush disasters, affecting tunnel constructionsafety. Due to the narrow tunnel space and the limited data that can be detected, the traditional linear inversion method for detecting them has multiple solutions. In this paper, we establish a database with complex models, including water‐bearing geological structures usually encountered during tunnel construction. Then we build a complex nonlinear relationship mapping between the tunnel face observation data and the resistivity model through the deep neural network algorithm (electrical resistivity inversion network tunnel, ERTInvNet‐T for short). ERTInvNet‐T first uses a fully connected network to extract the tunnel depth features from the observed data, from which a three‐dimensional geoelectric model of the tunnel is then generated by a three‐dimensional deconvolutional network. At the same time, there is a problem with the uneven spatial distribution of the sensitivity of the data to the model. Therefore, depth weighting information constraint based on the distance factor is added to the loss function, which improves the network algorithm's learning ability for different detection positions of the tunnel. The validity of the proposed method is verified by a large number of numerical simulation experiments.

Study on the Deformation Induced by Vertical Two-Layer Large Diameter Pipe-Jacking in the Soil-Rock Composite Stratum

Aiming at the features of deformation caused by large diameter vertical two-layer pipe jacking in the soil-rock composite stratum, on-site monitoring and numerical analysis has been done based on an electric power tunnel project constructed with the pipe jacking method, in which the upper tunnel is located in the soil layer and the lower tunnel is partially located in the rock layer. The research shows that: (1) During upper pipe jacking construction, the maximum transverse and longitudinal ground settlements are about three times those of the lower pipe jacking construction, and the maximum horizontal lateral displacement is about 3.3 times the lower pipe jacking construction. (2) Total ground settlement increases rapidly with the reduction of vertical clear spacing of the upper and lower pipe, and the superimposed effect should be taken into consideration during the vertical arranged pipe-jacking construction. (3) The Peck formula, which is used to estimate lateral surface subsidence distribution, is modified to make it more applicable in the soil–rock composite stratum to calculate the ground settlement induced by the construction of pipe-jacking.

Computational Micromechanics Investigation of Percolation and Effective Electro-Mechanical Properties of Carbon Nanotube/Polymer Nanocomposites using Stochastically Generated Realizations: Effects of Orientation and Waviness.

The electrical and mechanical properties of carbon nanotube/polymer nanocomposites depend strongly upon several factors such as CNT volume fraction, CNT alignment, CNT dispersion and CNT waviness among others. This work focuses on obtaining estimates and distribution for the effective electrical conductivity, elastic constants and piezoresistive properties as a function of these factors using a stochastic approach with numerous CNT/polymer realizations coupled with parallel computation. Additionally, electrical percolation volume fraction and percolation transitional behavior is also studied. The effective estimates and percolation values were found to be in good agreement with experimental works in the literature. It was found that with increasing CNT volume fraction, the mechanical properties improved. However, due to the interaction of CNTs with one another through electrical tunneling, the conductivity and piezoresistivity properties evolved in a more complex manner. While the degree of alignment played a strong role in the effective properties making them anisotropic, the effect of waviness was found to be insubstantial.

Risk Assessment and Prediction of Underground Utility Tunnels Based on Bayesian Network: A Case Study in Beijing, China

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China’s large cities. Currently, most of the underground utility tunnels’ risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the K2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses.

GaAs-based near-field thermophotonic devices: Approaching the idealized case with one-dimensional PN junctions

Thermophotonics (TPX) is a technology close to thermophotovoltaics (TPV), where a heated light-emitting diode (LED) is used as the active thermal emitter of the system. It allows to tune the heat flux, by means of electroluminescence, to a spectral range matching better the gap of a photovoltaic cell. The concept is extended to near-field thermophotonics (NF-TPX), where enhanced energy conversion is due to both electric control and wave tunnelling. We perform a thorough numerical analysis of a GaAs-based NF-TPX device, by coupling a near-field radiative heat transfer solver based on fluctuational electrodynamics with an algorithm based on a simplified version of the drift–diffusion equations in 1D. This allows for the investigation of the emission and absorption profiles in the LED and the photovoltaic (PV) cell, and for the scrutiny of the impact of key parameters. We also demonstrate that the performance obtained with this algorithm can approach idealized cases for improved devices. For the considered simplified architecture and 300 K temperature difference, we find a power density output of 1 W cm − 2 , underlining the potential for waste heat harvesting close to ambient temperature. • Near-field thermophotonics allows waste-heat harvesting of moderate temperatures. • GaAs is a promising material for the thermophotonic elements: LED and photovoltaic cell. • Thorough modelling of the 1D pn junction architecture is performed. • About 35 W m −2 K −1 can be produced if radiative recombinations are limited.

Single- and Twin-Photons Emitted from Fiber-Coupled Quantum Dots in a Distributed Bragg Reflector Cavity.

In this work, we develop single-mode fiber devices of an InAs/GaAs quantum dot (QD) by bonding a fiber array with large smooth facet, small core, and small numerical aperture to QDs in a distributed Bragg reflector planar cavity with vertical light extraction that prove mode overlap and efficient output for plug-and-play stable use and extensive study. Modulated Si doping as electron reservoir builds electric field and level tunnel coupling to reduce fine-structure splitting (FSS) and populate dominant XX and higher excitons XX+ and XXX. Epoxy package thermal stress induces light hole (lh) with various behaviors related to the donor field: lh h1 confined with more anisotropy shows an additional XZ line (its space to the traditional X lines reflects the field intensity) and larger FSS; lh h2 delocalized to wetting layer shows a fast h2–h1 decay; lh h2 confined shows D3h symmetric higher excitons with slow h2–h1 decay and more confined h1 to raise h1–h1 Coulomb interaction.

A novel temperature-dependent percolation model for the electrical conductivity and piezoresistive sensitivity of carbon nanotube-filled nanocomposites

Monte Carlo simulation is paired with a percolation model to analytically investigate the temperature effect and carbon nanotube (CNT) dispersion state on the temperature-dependent piezoresistive conductive network nanocomposite. The charge transport switch from electrical tunneling to elevated temperature quantum tunneling is studied based on a temperature-dependent tunneling model. The percolation network theory searches for the shortest distance of any CNT line segments scattered in the representative volume element to form possible interconnections and tunneling resistance approach evaluates resistivity by recognizing the conductive percolating paths. The effect of different factors such as the height of barrier potential, CNT length, degree of orientation and the cutoff distance on the resistance of the nanocomposite which is mainly caused by the inter-tube tunneling resistance is studied through analytical parametric studies. It is shown that the strain sensitivity of reinforced nanocomposite is increased as a result of the uniform dispersion of short aligned CNTs.

Mitigating massive triboelectric charging of drill in shadowed region of Moon

ABSTRACT A scheme based on natural quantum electric field tunnelling to mitigate the substantial charge deposition due to triboelectric (frictional) charging from the drill set-up, operating in the shadowed region of the Moon, is presented. We have shown that the micro (nano) structuring of the surface of the drill set-up might efficiently support the charge dissipation and mitigate the massive charge deposition (i.e. from hundreds of kV to tens of MV) to a significantly lower magnitude (e.g. few tens of V). Physically, the micro (nano) tips act as field emission (FE) centres and generate sufficiently large FE current through quantum field tunnelling to compensate for the negative tribocharging current. Our calculations demonstrate that the instrument surface fabricated with 10 nm spherical tips, operating in the electron-rich region within the permanently shadowed crater, maintains itself to a much lower negative potential of ∼23 V – it significantly contrasts with the case of planar surfaces where the tribocharging dominates and develops a substantial negative potential of the order of ∼100 MV.

Influence of an External Electric Field and Dissipative Tunneling on Recombination Radiation in Quantum Dots.

The effect of an external electric field and dissipative tunneling on the spectral intensity of recombination radiation in a quantum dot with an A+ + e impurity complex (a hole localized on a neutral acceptor interacting with an electron localized in the ground state of the quantum dot) is studied in the zero-radius potential model in the adiabatic approximation. The probability of dissipative tunneling of a hole is calculated in the one-instanton approximation. A high sensitivity of the recombination radiation intensity to the strength of the external electric field and to such parameters of the surrounding matrix (dissipative tunneling parameters) as temperature, the constant of interaction with the contact medium (or the heat-bath), and the frequency of phonon modes, has been revealed. It is shown that an external electric field leads to a shift of the recombination radiation threshold by several tens of meV, and a change in the parameters of dissipative tunneling has a noticeable effect on the spectral intensity of recombination radiation. It is shown that the resonant tunneling effect manifests itself in the form of “dips” in the field dependence of the spectral intensity of recombination radiation, which occur at certain values of the external electric field strength and temperature. This opens up certain prospects for the use of the considered system “quantum dot—impurity complex A+ + e” under conditions of dissipative tunneling for the study and diagnostics of biological objects.

Statistical analysis of effective electro-mechanical properties and percolation behavior of aligned carbon nanotube/polymer nanocomposites via computational micromechanics

CNT (carbon nanotube) embedded polymer nanocomposites exhibit significant variation in their electro-mechanical properties depending upon factors such as CNT volume fraction, CNT dispersion, CNT alignment and properties of the polymer. Of interest is electrical percolation where the electrical conductivity of the CNT/polymer nanocomposite increases several orders of magnitude with increase in CNT volume fraction. Estimates and distributions for the electrical conductivity and piezoresistive coefficients of the CNT/polymer nanocomposite are obtained and analyzed with increasing CNT volume fraction and varying barrier potential, a parameter that controls the extent of electrical tunneling. The barrier potential is seen to play a key role in determining electrical percolation in terms of the CNT volume fractions that correspond to this percolation transition. The effect of CNT alignment is analyzed by comparing the electro-mechanical properties in the alignment direction versus the transverse direction, where it is found that the properties in the alignment direction are larger than that of the transverse direction. Estimates of piezoresistive coefficients are converted into gage factors and discussed in the context of experimental sources in literature. The methodology for this work involves generating several semi-random 5μm×5μm computational realizations for different CNT volume fractions with randomized CNT seeding. These realizations are then analyzed using finite elements to obtain volume averaged effective values, which are then subsequently used to generate estimates (mean from several realizations) for mechanical stiffness, electrical conductivity and piezoresistive coefficients. The distribution in these properties is also studied using measures of coefficient of variation, skewness and kurtosis. It is found that electrical percolation can be captured by the transition of these measures of variation from low to high and back down to low values with increase in CNT volume fraction.

Electronic Transport Through Double Quantum Dot Coupled to Majorana Bound States and Ferromagnetic Leads

We have studied theoretically the properties of electrical current and tunnel magnetoresistance (TMR) through a serially connected double quantum dot (DQD) sandwiched between two ferromagnetic leads by using the nonequilibrium Green’s function technique. We consider that each of the DQD couples to one mode of the Majorana bound states (MBSs) formed at the ends of a topological superconductor nanowire with spin-dependent coupling strength. By adjusting the sign of the spin polarization of dot–MBS coupling strength and the arrangement of magnetic moments of the two leads, the currents’ magnitude can be effectively enhanced or suppressed. Under some conditions, a negative TMR emerges which is useful in detection of the MBSs, a research subject currently under extensive investigations. Moreover, the amplitude of the TMR can be adjusted in a large regime by variation of several system parameters, such as direct hybridization strength between the MBSs or the dots and the positions of the dots’ energy levels. Such tunable currents and TMR may also find use in high-efficiency spintronic devices or information processes.

Thermionic emission or tunneling? The universal transition electric field for ideal Schottky reverse leakage current: A case study in β -Ga2O3

The reverse leakage current through a Schottky barrier transitions from a thermionic emission-dominated regime to a barrier tunneling-dominated regime as the surface electric field increases. In this study, we evaluate such a transition electric field (ET) in β-Ga2O3 using a numerical reverse leakage model. ET is found to depend on temperature but has an extremely weak dependence on the doping concentration and the barrier height; as a result, a simple empirical expression can be derived to capture this near-universal dependence of ET on temperature. With the help of a field-plate design, we observed experimentally in lightly doped Ga2O3 Schottky barrier diodes near-ideal bulk reverse leakage characteristics, which match well with our numerical model and that confirm the presence of the transition region. Near the transition electric field, both thermionic emission and barrier tunneling should be considered. This study provides important guidance toward accurate design and modeling of Schottky barrier diodes, which can be readily extended to other semiconductors.