Solid-state optical properties of self-assembling amyloid-like peptides with different charged states at the terminal ends

The self-assembling of small peptides not only leads to the formation of intriguing nanoarchitectures, but also generates materials with unexpected functional properties. Oligopeptides can form amyloid-like cross-β assemblies that are able to emit intrinsic photoluminescence (PL), over the whole near-UV/visible range, whose origin is still largely debated. As proton transfer between the peptide chain termini within the assembly is one of the invoked interpretations of this phenomenon, we here evaluated the solid state PL properties of a series of self-assembled hexaphenylalanine peptides characterized by a different terminal charge state. Overall, our data indicate that the charge state of these peptides has a marginal role in the PL emission as all systems exhibit very similar multicolour PL associated with a violation of the Kasha’s rule. On the other hand, charged/uncharged ends occasionally produce differences in the quantum yields. The generality of these observations has been proven by extending these analyses to the Aβ16–21 peptide. Collectively, the present findings provide useful information for deciphering the code that links the spectroscopic properties of these assemblies to their structural/electronic features.

Analogous Atomic and Electronic Properties between V N and V N C B Defects in Hexagonal Boron Nitride

We investigate defect properties in hexagonal boron nitride (hBN) which is attracting much attention as a single photon emitter. Using first-principles calculations, we find that nitrogen-vacancy defect V N has a lower energy structure in C 1 h symmetry in 1− charge state than the previously known D 3 h symmetry structure. Noting that carbon has one more valence electron than boron species, our finding naturally points to the correspondence between V N and V N C B defects with one charge state difference between them, which is indeed confirmed by the similarity of atomic symmetries, density of states, and excitation energies. Since V N C B is considered as a promising candidate for the source of single photon emission, our study suggests V N as another important candidate worth attention, with its simpler form without the incorporation of foreign elements into the host material.

An LLC and LCL-T Resonant Tanks Based Topology for Battery Charger Application

To achieve the constant current (CC) and constant voltage (CV) charge of the lithium battery, the traditional LLC resonant converter requires the switching frequency varies in a wide range, which brings difficulty to the magnetic components design, and the system efficiency would also be degraded. In this article, a novel topology based on LLC and LCL-T resonant tanks is proposed to reduce the range of operating switching frequency. During the CC charge state, the proposed converter is operating with the LCL-T resonant tank, and it can be regarded as a current source, which provides constant charging current to the battery. During the CV charge state, the LCL-T resonant tank is bypassed and the structure of the proposed converter is modified to a traditional LLC resonant converter, and it is functioning as a CV source. Owing to the high accuracy of the CC and voltage sources, the required operating switching frequency range can be significantly reduced when compared with traditional LLC approaches. Operational principles and design guidelines for the proposed converter are described. Experiment and simulation results from a 180 W prototype are provided to validate the theoretical analysis.

Miniaturizing neural networks for charge state autotuning in quantum dots

A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots (QDs), the gate voltages required to stabilize quantized charges are unique for each individual qubit, resulting in a high-dimensional control parameter space that must be tuned automatically. Machine learning techniques are capable of processing high-dimensional data—provided that an appropriate training set is available—and have been successfully used for autotuning in the past. In this paper, we develop extremely small feed-forward neural networks that can be used to detect charge-state transitions in QD stability diagrams. We demonstrate that these neural networks can be trained on synthetic data produced by computer simulations, and robustly transferred to the task of tuning an experimental device into a desired charge state. The neural networks required for this task are sufficiently small as to enable an implementation in existing memristor crossbar arrays in the near future. This opens up the possibility of miniaturizing powerful control elements on low-power hardware, a significant step towards on-chip autotuning in future QD computers.