Search Results (4,514)

Nanohybrids consisting of quantum dots and graphene (QD/graphene) provides a unique scheme to design quantum sensors. The quantum confinement in QDs enables spectral tunability, while that in graphene provides superior photocarrier mobility. The combination of them allows for broadband light absorption and high photoconduction gain that in turn leads to high photoresponsivity in QD/Gr nanohybrid photodetectors. Since the first QD/graphene photodetector was reported in 2012, intensive research has been conducted on this topic. In this paper, a review of the recent progress made on QD/Gr nanohybrid photodetectors will be provided. Among many applications, there will be a particular focus on broadband and flexible photodetectors, which make use of the inherent advantages of the QD/Gr nanohybrids. The remaining challenges and future perspectives will be discussed in this emerging topic area. Full article

This review explores the variety of diamond-based sensing applications, emphasizing their material properties, such as high Young’s modulus, thermal conductivity, wide bandgap, chemical stability, and radiation hardness. These diamond properties give excellent performance in mechanical, pressure, thermal, magnetic, optoelectronic, radiation, biosensing, quantum, and other applications. In vibration sensing, nano/poly/single-crystal diamond resonators operate from MHz to GHz frequencies, with high quality factor via CVD growth, diamond-on-insulator techniques, and ICP etching. Pressure sensing uses boron-doped piezoresistive, as well as capacitive and Fabry–Pérot readouts. Thermal sensing merges NV nanothermometry, single-crystal resonant thermometers, and resistive/diode sensors. Magnetic detection offers FeGa/Ti/diamond heterostructures, complementing NV. Optoelectronic applications utilize DUV photodiodes and color centers. Radiation detectors benefit from diamond’s neutron conversion capability. Biosensing leverages boron-doped diamond and hydrogen-terminated SGFETs, as well as gas targets such as NO₂/NH₃/H₂ via surface transfer doping and Pd Schottky/MIS. Imaging uses AFM/NV probes and boron-doped diamond tips. Persistent challenges, such as grain boundary losses in nanocrystalline diamond, limited diamond-on-insulator bonding yield, high temperature interface degradation, humidity-dependent gas transduction, stabilization of hydrogen termination, near-surface nitrogen-vacancy noise, and the cost of high-quality single-crystal diamond, are being addressed through interface and surface chemistry control, catalytic/dielectric stack engineering, photonic integration, and scalable chemical vapor deposition routes. These advances are enabling integrated, high-reliability diamond sensors for extreme and quantum-enhanced applications. Full article

Radar systems serve as foundational components in both civil and military aerospace infrastructures. Modern radar must not only distinguish between detection and non-detection but must also classify detected objects. Signal processing increasingly integrates machine learning models into complex systems, such as radar. Additionally, developments have fused signal processing with quantum computing, creating an emerging field of research. This paper examines the applicability of quantum machine learning models for radar signal classification, focusing on the impact of Ansatz depth on expressibility. Multiple challenges arise due to the immature state of noisy intermediate-scale quantum hardware and the computational complexity of quantum circuit simulation. Nonetheless, results indicate that shallow Ansätze with fewer than 70 gates are sufficient to achieve the maximum available performance per data-encoding operation. Full article

A scale-dependent effective temperature emerges as a unifying principle in the statistical physics of apparently different phenomena, namely quantum confinement in finite-size systems and non-equilibrium effects in thermodynamic systems. This concept effectively maps these inherently complex systems onto equilibrium states, thereby enabling the direct application of standard statistical physics methods. By offering a framework to analyze these systems as effectively at equilibrium, our approach provides powerful new tools that significantly expand the scope of the field. Just as the constant speed of light in Einstein’s theory of special relativity necessitates a relative understanding of space and time, our fixed ratio of energy to temperature suggests a fundamental rescaling of both quantities that allows us to recognize shared patterns across diverse materials and situations. Full article

Effective in situ pH sensing holds exciting prospects in environmental and biomedical applications, but still faces a great challenge. Until now, pH sensors with small size, high sensitivity, good stability and repeatability, great biosafety, wide detection range, and flexible structure have rarely been reported. Herein, we propose a novel dual-emission ratiometric fluorescent pH sensor by decorating ethyl cellulose (EC)-encapsulated CdSe/ZnS quantum dots (QDs) and oxazine 170 perchlorate (O170 dye) on the surface of the spider silk. When a 473 nm excitation light is coupled into the pH sensor, the evanescent wave transmitting along the surface of the spider silk will excite the CdSe/ZnS QDs and then the O170 dye based on the fluorescence resonance energy transfer (FRET) effect from the QDs; thus, the pH sensing of the surrounding liquid environment can be achieved in real time by collecting the photoluminescence (PL) spectra of the pH sensor and measuring the emission intensity ratio of the two fluorescent materials. The sensor has also demonstrated a high sensing sensitivity (0.775/pH unit) within a wide pH range of 1.92–12.11, as well as excellent reusability and reversibility, structure and time stability, biocompatibility, and biosafety. The proposed pH sensor has a potential application in an in situ monitor of water microenvironments, cellular metabolism, tumor microenvironments, etc. Full article

We investigate optical bistability (OB) in a hybrid system comprising a semiconductor quantum dot (SQD), a metallic nanoshell (MNS), and a cell membrane within the framework of the multipole approximation. Bistability phase diagrams plotted in the system’s parameter subspaces demonstrate that, in the weak exciton–phonon coupling regime, dynamic switching of bistable states among no-channel, single-channel, and dual-channel configurations can be achieved via precise modulation of the MNS’s dielectric shell thickness. Especially, a critical sensing threshold is identified: the absorption peak disappears and a bistable effect emerges when only 1.82% of normal cells undergo malignant transformation. Furthermore, the bistable region exhibits a gradual broadening trend with an increasing proportion of cancerous cells, yielding a quantitative and ultra-sensitive readout that underpins a highly accurate strategy for early cancer diagnosis. These findings not only deepen our fundamental understanding of bistability regulation in hybrid quantum-plasmonic systems interfaced with biological materials but also offer valuable insights for the development of next-generation optical switches and biomedical sensing platforms. Full article

Omics research is changing the way medicine develops new strategies for diagnosis, prevention, and treatment. With the surge of advanced machine learning models tailored for omicss analysis, recent research has shown improved results and pushed the progress towards personalized medicine. The dissection of multiple layers of genetic information has provided new insights into precision medicine, at the same time raising issues related to data abundance. Studies focusing on single-cell scale have upgraded the knowledge about gene expression, revealing the heterogeneity that governs the functioning of multicellular organisms. The amount of information gathered through such sequencing techniques often exceeds the human capacity for analysis. Understanding the underlying network of gene expression regulation requires advanced computational tools that can deal with the complex analytical data provided. The recent emergence of artificial intelligence-based frameworks, together with advances in quantum algorithms, has the potential to enhance multiomicsc analyses, increasing the efficiency and reliability of the gene expression profile prediction. The development of more accurate computational models will significantly reduce the error rates in interpreting large datasets. By making analytical workflows faster and more precise, these innovations make it easier to integrate and interrogate multi-omics data at scale. Deep learning (DL) networks perform well in terms of recognizing complex patterns and modeling non-linear relationships that enable the inference of gene expression profiles. Applications range from direct prediction of DNA sequence-informed predictive modeling to transcriptomic and epigenetic analysis. Quantum computing, particularly through quantum machine learning methods, is being explored as a complementary approach for predictive modeling, with potential applications to complex gene interactions in increasingly large and high-dimensional biological datasets. Together, these tools are reshaping the study of complex biological data, while ongoing innovation in this field is driving progress towards personalized medicine. Overall, the combination of high-resolution omics and advanced computational tools marks an important shift toward more precise and data-driven clinical decision-making. Full article

Buried ferromagnetic targets such as unexploded ordnance generate an additional magnetic field to the main geomagnetic field, which manifests as a magnetic anomaly signal for localization. This paper presents an alternative scheme for localization by using a rotating magnetic sensor array and a joint optimization algorithm. Multiple magnetic sensors are integrated into an automated rotating measurement platform to achieve efficient and convenient data acquisition. To solve the target’s position coordinates, we combine quantum particle swarm optimization (QPSO) with the genetic algorithm (GA) to develop a joint optimization algorithm, which we name QPSO-GA. The proposed algorithm incorporates QPSO’s advantages of rapid convergence and local refined search with the advantages of global exploration and diversity preservation from the GA. Field experiments demonstrate that the proposed measurement system and algorithm achieve an average localization error of less than ten centimeters in a scenario with multiple sensors for multiple targets within a survey area of 4 m by 4 m, meeting general application requirements. Full article

This review provides a comprehensive analysis of modern strategies for the synthesis, functionalization, and application of carbon nanotubes (CNTs) and graphene for the development of high-performance polymer composites in the field of strain sensing. The paper systematically organizes key synthesis methods for CNTs and graphene (chemical vapor deposition (CVD), such as arc discharge, laser ablation, microwave synthesis, and flame synthesis, as well as approaches to their chemical and physical modification aimed at enhancing dispersion within polymer matrices and strengthening interfacial adhesion. A detailed examination is presented on the structural features of the nanofillers, such as the CNT aspect ratio, graphene oxide modification, and the formation of hybrid 3D networks and processing techniques, which enable the targeted control of the nanocomposite’s electrical conductivity, mechanical strength, and flexibility. Central focus is placed on the fundamental mechanisms of the piezoresistive response, analyzing the role of percolation thresholds, quantum tunneling effects, and the reconfiguration of conductive networks under mechanical load. The review summarizes the latest advancements in flexible and stretchable sensors capable of detecting both micro- and macro-strains for structural health monitoring, highlighting the achieved improvements in sensitivity, operational range, and durability of the composites. Ultimately, this analysis clarifies the interrelationship between nanofiller structure (CNTs and graphene), processing conditions, and sensor functionality, highlighting key avenues for future innovation in smart materials and wearable devices. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data. Full article

Quantum imaging leverages entanglement and photon correlations to surpass classical limits in resolution and noise performance. However, its practical deployment is constrained by bulky optical setups and limited system adaptability. Metasurfaces—ultrathin, subwavelength-structured devices—offer a compact and reconfigurable solution for wavefront control in quantum light fields. This review presents recent advances in geometric-, propagation-, and hybrid-phase metasurface designs, showcasing their contributions to enhanced spatial resolution, improved visibility, and system miniaturization across applications such as ghost imaging, quantum holography, and single-photon microscopy. It also examines key challenges—including photon loss, fabrication-induced phase noise, and the lack of dynamic tunability—while outlining future directions for developing integrated, noise-resilient, and task-specific quantum imaging platforms. Full article

As one of the payloads on board the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) spacecraft, the ultraviolet imager (UVI) aims to capture N2 Lyman–Birge–Hopfield (LBH) aurora continuously on a high-eccentricity orbit. The UVI instrument includes an intensified charge-coupled device (ICCD) for far ultraviolet (FUV) wavelength. It comprises a sealed image intensifier, a relay lens system, a CCD, and a mechanical housing. ICCD’s performance characteristics are evaluated before integrating with the optical system of the UVI, including the quantum efficiency, radiant gain, background characteristics, excess noise factor, image quality, and signal-to-noise ratio (SNR). The testing procedure and results are presented and discussed. The results demonstrate that the comprehensive performance of the detector is good, and provide critical technical support for quantitative applications. Full article

Non-destructive monitoring of fruit ripening is essential for optimising harvest time, yet its application to tropical viticulture remains largely unexplored. This study evaluated in situ chlorophyll a fluorescence as a non-invasive physiological marker to track berry development and metabolic maturation in two table grape cultivars (Vitis labrusca L. var. Niagara Rosada and var. Romana) under tropical field conditions, characterised by the latitude position, absence of chilling-induced dormancy, and variable rainfall during ripening. Berries’ fluorescence parameters (Fo, Fm, Fv and Fv/Fm) were monitored weekly from the pea-size stage to commercial harvest (67–123 days after pruning) using a portable modulated fluorometer, along with chlorophyll and quality trait measurements. A decline in fluorescence parameters during maturation coincided with chlorophyll degradation and the accumulation of glucose and fructose. The maximum quantum yield of PSII (Fv/Fm) remained stable (≈0.75) throughout development, indicating sustained photochemical efficiency despite chloroplast disassembly. Significant correlations (r > 0.80) were established between fluorescence parameters and key maturity indices, with distinct cultivar-specific patterns evident between the NR and RM cultivars. Therefore, chlorophyll a fluorescence provided a reliable, portable, non-destructive tool for monitoring ripening dynamics and estimating quality parameters in table grapes, offering practical advantages for tropical viticulture where environmental variability demands flexible monitoring. Full article

Phosphorus-31 nuclear magnetic resonance (³¹P NMR) spectroscopy is a powerful analytical physical chemistry experimental technique that is widely used to study the structure and dynamics of phosphorus-containing compounds today. Accurate calculation of ³¹P NMR chemical shifts lies in the basis of the proper assignment of NMR signals, as they can be closely spaced to each other in the NMR spectra of systems that bear nuclei with subtly different electron environments, like complex organophosphorus compounds, nucleic acids, and phosphates, etc. The most advanced quantum chemistry (QC) methods allow us to reach the agreement between theoretical values of ³¹P NMR chemical shifts and experiments within a few ppm, which makes them a useful tool for studying chemical structure, reaction mechanisms, and catalyst design with the aid of the NMR method. This review surveys the application of both density functional and wave function methods of electron structure to the calculation of ³¹P NMR chemical shifts and proposes a thorough discussion of the latest findings related to the factors affecting the final accuracy of the ³¹P NMR chemical shifts prediction, including basis sets, the geometry factor effect, solvent, vibrational, and relativistic corrections. Full article

We demonstrate a complete and experimentally validated self-testing protocol for two-qubit partially entangled states, which avoids the need for full tomographic reconstruction. Using a room-temperature type-II PPKTP polarization-entangled source and a free-space optical setup, we implement both quantum state tomography and optimal generalized Bell measurements within a single system. Our approach achieves high-fidelity self-testing of non-maximally entangled states under black-box assumptions, establishing a solid foundation for device-independent quantum information processing applications. Full article