Flexoelectricity Driven Fano Resonance in Slotted Carbon Nanotubes for Decoupled Multifunctional Sensing

Multifunctionality, interference-free signal readout, and quantum effect are important considerations for flexible sensors equipped within a single unit towards further miniaturization. To address these criteria, we present the slotted carbon nanotube (CNT) junction features tunable Fano resonance driven by flexoelectricity, which could serve as an ideal multimodal sensory receptor. Based on extensive ab initio calculations, we find that the effective Fano factor can be used as a temperature-insensitive extrinsic variable for sensing the bending strain, and the Seebeck coefficient can be used as a strain-insensitive intrinsic variable for detecting temperature. Thus, this dual-parameter permits simultaneous sensing of temperature and strain without signal interference. We further demonstrate the applicability of this slotted junction to ultrasensitive chemical sensing which enables precise determination of donor-type, acceptor-type, and inert molecules. This is due to the enhancement or counterbalance between flexoelectric and chemical gating. Flexoelectric gating would preserve the electron–hole symmetry of the slotted junction whereas chemical gating would break it. As a proof-of-concept demonstration, the slotted CNT junction provides an excellent quantum platform for the development of multistimuli sensation in artificial intelligence at the molecular scale.

Quantification of microbial robustness

Stable cell performance in a fluctuating environment is essential for sustainable bioproduction and synthetic cell functionality; however, microbial robustness is rarely quantified. Here, we describe a high-throughput strategy for quantifying robustness of multiple cellular functions and strains in a perturbation space. We evaluated quantifications theory on experimental data and concluded that the mean-normalized Fano factor allowed accurate, reliable, and standardized quantification. Our methodology applied to perturbations related to lignocellulosic bioethanol production showed that Saccharomyces cerevisiae Ethanol Red exhibited both higher and more robust growth rates than CEN.PK and PE-2, while a more robust product yield traded off for lower mean levels. The methodology validated that robustness is function-specific and characterized by positive and negative function-specific trade-offs. Systematic quantification of robustness to end-use perturbations will be important to analyze and construct robust strains with more predictable functions.

Tunneling effect in gapped graphene disk in magnetic flux and electrostatic potential

We investigate the tunneling effect of a Corbino disk in graphene in the presence of a variable magnetic flux Φi created by a solenoid piercing the inner disk under the effect of a finite mass term in the disk region (R1 < r < R2) and an electrostatic potential. Considering different regions, we explicitly determine the associated eigenspinors in terms of Hankel functions. The use of matching conditions and asymptotic behavior of Hankel functions for large arguments, enables us to calculate transmission and other transport quantities. Our results show that the energy gap suppresses the tunneling effect by creating singularity points of zero transmission corresponding to the maximum shot noise peaks quantified by the Fano factor F . The transmission as a function of the radii ratio R2/R1 becomes oscillatory with a decrease in periods and amplitudes. It can even reach one (Klein tunneling) for large values of the energy gap. The appearance of the minimal conductance at the points kF R1 = R1δ is observed. Finally we find that the electrostatic potential can control the effect of the band gap.

Variability of neuronal responses in the posterior superior temporal sulcus predicts choice behavior during social interactions

Recent studies have shown that neural activity in a well-defined patch in the posterior superior temporal sulcus (the "gaze following patch", GFP) of the primate brain is strongly modulated when the other´s gaze attracts the observer's attention to locations/objects, the other is looking at. Changes of the mean discharge rate of neurons in the monkey GFP indicate that they are involved in two distinct computations: the allocation of spatial attention guided by the other´s gaze vector and the suppression of gaze following if inappropriate in a given situation. Here we asked if and how the discharge variability of neurons in the GFP is related to the task and, furthermore, if it carries information on behavioral performance. To this end, we calculated the Fano factor as a measure of across-trial discharge variability as a function of time. Our results show that all neurons exhibiting a task-related discharge-rate modulation also exhibit a stimulus onset-dependent drop in the Fano factor. Furthermore, the amplitude of the Fano factor reduction is modulated by task condition and the neuron's selectivity in this regard. We found that these effects are directly related to the monkeys' behavioral performance in that the Fano factor is predictive about upcoming correct or wrong decisions. Our results indicate that neuronal discharge variability as gauged by the Fano-factor, hitherto primarily studied in the context of visual perception or motor control, is an informative measure also in studies of the neural underpinnings of complex social behavior.

Unified Model of Shot Noise in the Tunneling Current in Sub-10 nm MOSFETs

A single unified analytical model is presented to predict the shot noise for both the source-to-drain (SD) and the gate tunneling current in sub-10 nm MOSFETs with ultrathin oxide. Based on the Landauer formula, the model is constructed from the sequential tunneling flows associated with number fluctuations. This approach provides the analytical formulation of the shot noise as a function of the applied voltages. The model performs well in predicting the Fano factor for shot noise in the SD and gate tunneling currents.

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-drain tunneling current through the potential barriers. By training a spatial distribution of the thermal channel noise and a Fano factor of the shot noise, underlying physical theories are naturally embedded into the PINN model as prior information. The PINN model shows good capability of predicting the noise performance at high frequencies.

Quantum Chaos in Time Series of Single Photons as a Superposition of Wave and Particle States

We build a time series of single photons with quantum chaos statistics, using a version of the Grangier anti-correlation experiment. The criteria utilized to determine the presence of quantum chaos is the frame of the Fano factor and the power spectrum. We also show that photons with chaotic statistics are in a balanced superposition of photons with both wave-like and particle like behaviors. To support the presence of quantum chaos, we study both Shannon’s entropy, and the complexity of single photons time series.

Relaxation processes in silicon heterojunction solar cells probed via noise spectroscopy

We have employed state-of-the-art cross-correlation noise spectroscopy (CCNS) to study carrier dynamics in silicon heterojunction solar cells (SHJ SCs). These cells were composed of a light absorbing n-doped monocrystalline silicon wafer contacted by passivating layers of i-a-Si:H and doped a-Si:H selective contact layers. Using CCNS, we are able to resolve and characterize four separate noise contributions: (1) shot noise with Fano factor close to unity due to holes tunneling through the np-junction, (2) a 1/f term connected to local potential fluctuations of charges trapped in a-Si:H defects, (3) generation-recombination noise with a time constant between 30 and 50 μs and attributed to recombination of holes at the interface between the ITO and n-a-Si:H window layer, and (4) a low-frequency generation-recombination term observed below 100 K which we assign to thermal emission over the ITO/ni-a-Si:H interface barrier. These results not only indicate that CCNS is capable of reveling otherwise undetectable relaxation process in SHJ SCs and other multi-layer devices, but also that the technique has a spatial selectivity allowing for the identification of the layer or interface where these processes are taking place.

Quantum Tunneling Mechanisms in Monolayer Graphene Modulated by Multiple Electrostatic Barriers

The transmission coefficient and electronic conductance of a graphene monolayer in the presence of multi-electrostatic barriers are theoretically investigated using the transfer matrix method (TMM). The transmission coefficient, conductance, and Fano factor are evaluated as a function of the number and width of the barriers, angle/energy of incidence, as well as the applied potential at each barrier. We find that the transmission coefficient presents a series of resonances that depends on the number and widths of the barriers. Furthermore, we show that the resonant states can be suppressed for larger incidence angles and barrier widths and tuned towards lower energies. Consequently, the proposed structure can be used to fabricate new optoelectronic devices based on (ON/OFF) states as tunable field-effect transistors.