Search Results (2,903)

In high energy density physics, the demand for precise detection of nanosecond-level fast physical processes is high. Ga:ZnO (GZO), GaN, and other fast scintillators are widely used in pulsed signal detection. However, many of them, especially wide-bandgap materials, still face issues of low luminous intensity and significant self-absorption. Therefore, an enhanced method was proposed to tune the wavelength of materials via coating perovskite quantum dot (QD) films. Three-layer samples based on GZO were primarily investigated and characterized. Radioluminescence (RL) spectra from each face of the samples, as well as their decay times, were obtained. Lower temperatures further enhanced the luminous intensity of the samples. Its overall luminous intensity increased by 2.7 times at 60 K compared to room temperature. The changes in the RL processes caused by perovskite QD and low temperatures were discussed using the light tuning and transporting model. In addition, an experiment under a pico-second electron beam was conducted to verify their pulse response and decay time. Accordingly, the samples were successfully applied in beam state monitoring of nanosecond pulsed proton beams, which indicates that GZO wafer coating with perovskite QD films has broad application prospects in pulsed radiation detection. Full article

A brief review of various existing mathematical formulations of the uncertainty relations in quantum mechanics, containing variances of two or more non-commuting operators, is given. In particular, inequalities for the products of higher-order moments of a coordinate and a momentum are considered, as well as inequalities making the uncertainty relations more accurate when additional information about a quantum system is available (for example, the correlation coefficient or the degree of mixing of a quantum state characterized by the trace of the squared statistical operator). The special cases of two, three, and four operators are discussed in detail. Full article

Rydberg atoms play a crucial role in testing atomic structure theory, quantum computing and simulation. Measurements of transition frequencies from the $2^{1,3}S$ states to Rydberg ${}^{1,3}P$ states have reached a precision of several kHz, which poses significant challenges for theoretical calculations, since the accuracy of variational energy calculations decreases rapidly with increasing principal quantum number n. Recently the complex “triple” Hylleraas basis was employed to attain the ionization energy of helium $24{}^{1}P$ state with high accuracy. Different from it, we extended the correlated B-spline basis functions (C-BSBFs) to calculate the Rydberg states of helium. The nonrelativistic energies of $1snp$${}^{1,3}P$ states up to $n=27$ achieve at least 14 significant digits using a unified basis set, thereby greatly reducing the complexity of the optimization process. Results of geometric structure parameters and cusp conditions were presented as well. Both the global operator and direct calculation methods are employed and cross-checked for contact potentials. This C-BSBF method not only obtains high-accuracy energies across all studied levels but also confirms the effectiveness of the C-BSBFs in depicting long-range and short-range correlation effects, laying a solid foundation for future high-accuracy Rydberg-state calculations with relativistic and QED corrections included in helium atom and low-Z helium-like ions. Full article

In this work, we demonstrate high-efficiency 385 nm AlGaN-based near-ultraviolet micro light emitting diode (NUV-Micro LED) arrays. The epi structure is prepared using a novel AlN-inserted superlattice electrical blocking layer which enhances hole spreading in the p-type region significantly. The NUV-Micro LED arrays in this work comprise 228 chips in parallel with wavelengths at 385 nm, and each single chip size is 15 × 30 μm². Compared with conventional bulk AlGaN-based EBL structures, the NUV-Micro LED arrays that implemented the new hole spreading enhanced superlattice electrical blocking layer (HSESL-EBL) structure proposed in this work had a remarkable increase in light output power (LOP) at current density, increasing the range down from 0.02 A/cm² to as high as 97 A/cm². The array’s light output power is increased up to 1540% at the lowest current density 0.02 A/cm², and up to 58% at the highest current density 97 A/cm², measured under room temperature (RT); consequently, the WPE is increased from 13.4% to a maximum of 30.18%. This AlN-inserted HESEL-EBL design significantly enhances both the lateral expansion efficiency and the hole injection efficiency into the multi quantum well (MQW) in the arrays, improving the concentration distribution of the holes in MQW while maintaining good suppression of electron leakage. The array’s efficiency droop has also been greatly reduced. Full article

The unique properties of graphene have allowed for the development of graphene-based field-effect transistors (GFETs) for applications in biosensors and chemical devices. However, the modeling and optimization of GFET performance exhibit great challenges. Herein, we propose a quantum transport simulation model for graphene-based field-effect transistors (GFETs) implemented in the open-source Octave programming language. The proposed simulation model (named SimQ) combines the Landauer–Büttiker formalism with self-consistent Schrödinger–Poisson solutions, enabling reliable simulations of transport phenomena. Our approach agrees well with established models, achieving Landauer–Büttiker transmission and tunneling transmission of 0.28 and 0.92, respectively, which are validated against experimental data. The model can predict key GFET characteristics, including carrier mobilities (500–4000 cm²/V·s), quantum capacitance effects, and high-frequency operation (80–100 GHz). SimQ offers detailed insights into charge distribution and wave function evolution, achieving an enhanced computational efficiency through optimized algorithms. Our work contributes to the modeling of graphene-based field-effect transistors, providing a flexible and accessible simulation platform for designing and optimizing GFETs with potential applications in the next generation of electronic devices. Full article

The focal “hot spot” neuropathologies in COVID-19 infection are revealing footprints of a hidden underlying collapse of a novel ultrafast ultradian Piezo2 signaling system within the nervous system. Paradoxically, the same initiating pathophysiology may underpin the systemic findings in COVID-19 infection, namely the multiorgan SARS-CoV-2 infection-induced vascular pathologies and brain–body-wide systemic pro-inflammatory signaling, depending on the concentration and exposure to infecting SARS-CoV-2 viruses. This common initiating microdamage is suggested to be the primary damage or the acquired channelopathy of the Piezo2 ion channel, leading to a principal gateway to pathophysiology. This Piezo2 channelopathy-induced neural switch could not only explain the initiation of disrupted cell–cell interactions, metabolic failure, microglial dysfunction, mitochondrial injury, glutamatergic synapse loss, inflammation and neurological states with the central involvement of the hippocampus and the medulla, but also the initiating pathophysiology without SARS-CoV-2 viral intracellular entry into neurons as well. Therefore, the impairment of the proposed Piezo2-induced quantum mechanical free-energy-stimulated ultrafast proton-coupled tunneling seems to be the principal and critical underlying COVID-19 infection-induced primary damage along the brain axes, depending on the loci of SARS-CoV-2 viral infection and intracellular entry. Moreover, this initiating Piezo2 channelopathy may also explain resultant autonomic dysregulation involving the medulla, hippocampus and heart rate regulation, not to mention sleep disturbance with altered rapid eye movement sleep and cognitive deficit in the short term, and even as a consequence of long COVID. The current opinion piece aims to promote future angles of science and research in order to further elucidate the not entirely known initiating pathophysiology of SARS-CoV-2 infection. Full article

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage—they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling. Full article

This paper serves as a comprehensive and practical resource to guide researchers in selecting suitable metals for use as photocathodes in radio-frequency (RF) and superconducting radio-frequency (SRF) electron guns. It offers an in-depth review of bulk and thin-film metals commonly employed in many applications. The investigation includes the photoemission, optical, chemical, mechanical, and physical properties of metallic materials used in photocathodes, with a particular focus on key performance parameters such as quantum efficiency, operational lifetime, chemical inertness, thermal emittance, response time, dark current, and work function. In addition to these primary attributes, this study examines essential parameters such as surface roughness, morphology, injector compatibility, manufacturing techniques, and the impact of chemical environmental factors on overall performance. The aim is to provide researchers with detailed insights to make well-informed decisions on materials and device selection. The holistic approach of this work associates, in tabular format, all photo-emissive, optical, mechanical, physical, and chemical properties of bulk and thin-film metallic photocathodes with experimental data, aspiring to provide unique tools for maximizing the effectiveness of laser cleaning treatment. Full article

The mechanism by which voltage-gated ion channels open and close has been the subject of intensive investigation for decades. For a large class of potassium channels and related sodium channels, the consensus has been that the gating current preceding the main ionic current is a large movement of positively charged segments of protein from voltage-sensing domains that are mechanically connected to the gate through linker sections of the protein, thus opening and closing the gate. We have pointed out that this mechanism is based on evidence that has alternate interpretations in which protons move. Very little literature considers the role of water and protons in gating, although water must be present, and there is evidence that protons can move in related channels. It is known that water has properties in confined spaces and at the surface of proteins different from those in bulk water. In addition, there is the possibility of quantum properties that are associated with mobile protons and the hydrogen bonds that must be present in the pore; these are likely to be of major importance in gating. In this review, we consider the evidence that indicates a central role for water and the mobility of protons, as well as alternate ways to interpret the evidence of the standard model in which a segment of protein moves. We discuss evidence that includes the importance of quantum effects and hydrogen bonding in confined spaces. K⁺ must be partially dehydrated as it passes the gate, and a possible mechanism for this is considered; added protons could prevent this mechanism from operating, thus closing the channel. The implications of certain mutations have been unclear, and we offer consistent interpretations for some that are of particular interest. Evidence for proton transport in response to voltage change includes a similarity in sequence to the H_v1 channel; this appears to be conserved in a number of K⁺ channels. We also consider evidence for a switch in -OH side chain orientation in certain key serines and threonines. Full article

In this position paper, we argue that the conventional understanding of ‘information’ (as generally conceived in science, in a digital fashion) is overly simplistic and not consistently applicable to living systems, which are open systems that cannot be reduced to any kind of ‘portion’ (building block) ascribed to the category of quantity. Instead, it is a matter of relationships and qualities in an indivisible analogical (and ontological) relationship between any presumed ‘software’ and ‘hardware’ (information/matter, psyche/soma). Furthermore, in biological systems, contrary to Shannon’s definition, which is well-suited to telecommunications and informatics, any kind of ‘information’ is the opposite of internal entropy, as it depends directly on order: it is associated with distinction and differentiation, rather than flattening and homogenisation. Moreover, the high degree of structural compartmentalisation of living matter prevents its energetics from being thermodynamically described by using a macroscopic, bulk state function. This requires the Second Principle of Thermodynamics to be redefined in order to make it applicable to living systems. For these reasons, any static, bit-related concept of ‘information’ is inadequate, as it fails to consider the system’s evolution, it being, in essence, the organized coupling to its own environment. From the perspective of quantum field theory (QFT), where many vacuum levels, symmetry breaking, dissipation, coherence and phase transitions can be described, a consistent picture emerges that portrays any living system as a relational process that exists as a flux of context-dependent meanings. This epistemological shift is also associated with a transition away from the ‘particle view’ (first quantisation) characteristic of quantum mechanics (QM) towards the ‘field view’ possible only in QFT (second quantisation). This crucial transition must take place in life sciences, particularly regarding the methodological approaches. Foremost because biological systems cannot be conceived as ‘objects’, but rather as non-confinable processes and relationships. Full article

Quantum sensing leverages quantum resources to enable ultra-precise measurements beyond classical limits, driving transformative advancements in metrology. Optical fiber quantum sensing, integrating optical fiber sensing with quantum technologies, enhances measurement precision and sensitivity from multiple perspectives, such as exploring high-sensitivity optical fiber sensing installations and generating high-quality optical fiber quantum states. Following decades of comprehensive investigations and remarkable advances in optical fiber quantum sensing technology, this review systematically examines research achievements in this field through two complementary perspectives: one is the basic principle of generating optical fiber quantum states and their applications in sensing and the other is optical fiber quantum interferometers and their applications in sensing. Finally, examine current opportunities and challenges as well as the future development of optical fiber quantum sensing. Full article

The general framework of Entropic Dynamics (ED) is used to construct non-relativistic models of relational Quantum Mechanics from well-known inference principles—probability, entropy and information geometry. Although only partially relational—the absolute structures of simultaneity and Euclidean geometry are still retained—these models provide a useful testing ground for ideas that will prove useful in the context of more realistic relativistic theories. The fact that in ED the positions of particles have definite values, just as in classical mechanics, has allowed us to adapt to the quantum case some intuitions from Barbour and Bertotti’s classical framework. Here, however, we propose a new measure of the mismatch between successive states that is adapted to the information metric and the symplectic structures of the quantum phase space. We make explicit that ED is temporally relational and we construct non-relativistic quantum models that are spatially relational with respect to rigid translations and rotations. The ED approach settles the longstanding question of what form the constraints of a classical theory should take after quantization: the quantum constraints that express relationality are to be imposed on expectation values. To highlight the potential impact of these developments, the non-relativistic quantum model is parametrized into a generally covariant form and we show that the ED approach evades the analogue of what in quantum gravity has been called the problem of time. Full article

The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection. Full article

Complex phase diagrams are a generic feature of quantum materials that display high-temperature superconductivity. In addition to d-wave superconductivity (or other unconventional states), these phase diagrams typically include various forms of charge-ordered phases, including charge-density waves and/or spin-density waves, as well as electronic nematic states. In most cases, these phases have critical temperatures comparable in magnitude to that of the superconducting state and appear in a “pseudo-gap” regime. In these systems, the high temperature state does not produce a good metal with well-defined quasiparticles but a ”strange metal”. These states typically arise from doping a strongly correlated Mott insulator. With my collaborators, I have identified these behaviors as a problem with “Intertwined Orders”. A pair-density wave is a type of superconducting state that embodies the physics of intertwined orders. Here, I discuss the phenomenology of intertwined orders and the quantum materials that are known to display these behaviors. Full article

The chiral nonlinear Schrödinger equation (CNLSE) serves as a simplified model for characterizing edge states in the fractional quantum Hall effect. In this paper, we leverage the generalization and parameter inversion capabilities of physics-informed neural networks (PINNs) to investigate both forward and inverse problems of 1D and 2D CNLSEs. Specifically, a hybrid optimization strategy incorporating exponential learning rate decay is proposed to reconstruct data-driven solutions, including bright soliton for the 1D case and bright, dark soliton as well as periodic solutions for the 2D case. Moreover, we conduct a comprehensive discussion on varying parameter configurations derived from the equations and their corresponding solutions to evaluate the adaptability of the PINNs framework. The effects of residual points, network architectures, and weight settings are additionally examined. For the inverse problems, the coefficients of 1D and 2D CNLSEs are successfully identified using soliton solution data, and several factors that can impact the robustness of the proposed model, such as noise interference, time range, and observation moment are explored as well. Numerical experiments highlight the remarkable efficacy of PINNs in solution reconstruction and coefficient identification while revealing that observational noise exerts a more pronounced influence on accuracy compared to boundary perturbations. Our research offers new insights into simulating dynamics and discovering parameters of nonlinear chiral systems with deep learning. Full article