Gas sensing is essential for detecting and measuring gas concentrations across various environments, with applications in environmental monitoring, industrial safety, and healthcare. The integration of two-dimensional (2D) materials, organic materials, and metal oxides has significantly advanced gas sensor technology, enhancing its sensitivity, selectivity, and response times at room temperature. This review examines the progress in optically activated gas sensors, with emphasis on 2D materials, metal oxides, and organic materials, due to limited studies on their use in optically activated gas sensors, in contrast to other traditional gas-sensing technologies. We detail the unique properties of these materials and their impact on improving the figures of merit (FoMs) of gas sensors. Transition metal dichalcogenides (TMDCs), with their high surface-to-volume ratio and tunable band gap, show exceptional performance in gas detection, especially when activated by UV light. Graphene-based sensors also demonstrate high sensitivity and low detection limits, making them suitable for various applications. Although organic materials and hybrid structures, such as metal–organic frameworks (MoFs) and conducting polymers, face challenges related to stability and sensitivity at room temperature, they hold potential for future advancements. Optically activated gas sensors incorporating metal oxides benefit from photoactive nanomaterials and UV irradiation, further enhancing their performance. This review highlights the potential of the advanced materials in developing the next generation of gas sensors, addressing current research gaps and paving the way for future innovations.

Exciton polaritons in metallic nanocavities and transition-metal dichalcogenide monolayers has led to striking discoveries, ranging from Bose-Einstein condensation to slowing light. Although plasmonic nanocavities offer small mode volumes, the intrinsic losses of plasmonic nanocavities remain an open challenge in exciton-polariton devices. Consequently, dielectric nanocavities are used as an alternative candidate due to their low intrinsic losses. However, large mode volumes are a central bottleneck in dielectric nanocavities. Here, we theoretically propose to use a hybrid dielectric-metallic nanocavity to enhance light-matter interactions between the excitons of the ${\mathrm{WS}}_2$ monolayer and the hybrid nanocavity. Such a hybrid nanoresonator inherits the advantages of both dielectric and metallic nanocavities, including ultrasmall mode volume, ultralow losses, and reasonably-high-Q factor. It is demonstrated that the thickness and material of the central gap film together with the thickness of the metallic substrate play vital roles in governing the coupling strength between 1L-${\mathrm{WS}}_2$ excitons and the cavity. After optimizing the geometry and material parameters, the Rabi splitting is increased to 113 meV, almost twice that in dielectric metasystems. The significant improvement can be attributed to the greatly enhanced near field and the ultrasmall mode volume. Furthermore, we show that Rabi splitting can be further boosted to 151 meV by increasing the number of layers of ${\mathrm{WS}}_2$ and h-$\mathrm{BN}$ film in the nanocavity.

Rydberg atoms are neutral atoms excited to high principal quantum number states, which endows them with exaggerated properties such as large electric dipole moments, long lifetimes, and extreme sensitivity to external electromagnetic fields. These characteristics form the foundation of Rydberg atom-based sensors, an emerging class of quantum devices capable of optically detecting electric fields across frequencies from DC to the terahertz regime. Rydberg-based electrometry operates through both Autler–Townes (AT) splitting of resonant Rydberg transitions and Stark-shift measurements for high-frequency or far-detuned fields, enabling broadband field sensing from DC to the THz regime. Using ladder-type electromagnetically induced transparency (EIT) and AT splitting, these sensors enable non-invasive, SI-traceable measurements of field amplitude, frequency, phase, and polarization. Recent developments have demonstrated broadband electric field probes, voltage calibration standards, and compact RF receivers based on thermal vapor cells and integrated photonic architectures. Furthermore, innovations in multi-photon EIT, superheterodyne readout, and multi wave mixing have expanded the dynamic range and bandwidth of Rydberg-based electrometry. Despite challenges related to environmental perturbations, linewidth broadening, and laser stabilization, ongoing advances in atomic control, hybrid photonic integration, and EIT-based readout promise scalable, chip-compatible sensors. This review summarizes the physical principles, experimental progress, and emerging applications of Rydberg atom-based sensing, emphasizing their potential for next generation quantum metrology, wireless communication, and precision field mapping.

All-inorganic halide perovskites have attracted significant interest in photodetector applications due to their remarkable photoresponse properties. However, the toxicity and instability of lead-based perovskites hinder their commercialization. In this work, we propose cubic Ba₃SbI₃ as a promising, environmentally friendly, lead-free material for next-generation photodetector applications. Ba₃SbI₃ shows good light absorption, low effective masses, and favorable elemental abundance and cost, making it a promising candidate compound for device applications. Its structural, mechanical, electronic, and optical properties were systematically investigated using density functional theory (DFT) with the Perdew–Burke–Ernzerhof (PBE) and hybrid HSE06 functionals. The material was found to be dynamically and mechanically stable, with a direct bandgap of 0.78 eV (PBE) and 1.602 eV (HSE06). Photodetector performance was then simulated in an Al/FTO/In₂S₃/Ba₃SbI₃/Sb₂S₃/Ni configuration using SCAPS-1D. To optimize device efficiency, the width, dopant level, and bulk concentration for each layer of the gadgets were systematically modified, while the effects of interface defects, operating temperature, and series and shunt resistances were also evaluated. The optimized device achieved an open-circuit voltage (Voc) of 1.047 V, short-circuit current density (Jsc) of 31.65 mA/cm², responsivity of 0.605 A W⁻¹, and detectivity of 1.05 × 10¹⁷ Jones. In contrast, in the absence of the Sb₂S₃ layer, the performance was reduced to a Voc of 0.83 V, Jsc of 26.8 mA/cm², responsivity of 0.51 A W⁻¹, and detectivity of 1.5 × 10¹⁵ Jones. These results highlight Ba₃SbI₃ as a promising platform for high-performance, cost-effective, and environmentally benign photodetectors.

Photodetectors (PDs) based on 4H silicon carbide (SiC) have garnered significant interest due to their exceptional optoelectronic properties. However, their photoresponse is typically restricted to the ultraviolet (UV) region, with limited light absorption beyond 380 nm, which constrains their utility in visible light detection applications. To overcome this limitation, an efficient photodetector was developed using an alloy with TaC (80%) and Cu (20%) on a 4H n-type SiC substrate, enabling effective light detection at 405 nm. The device exhibited high performance with a high photoresponsivity of 1.66 ${\mathrm{A}\mathrm{W}}^{-1}$ and a specific detectivity of $2.69\times {10}^8$ Jones at 405 nm. The superior performance of the device is ascribed to the enhanced electrical conductivity and optical absorption of the TaC: Cu layer on the 4H SiC substrate, particularly in the near-ultraviolet region. This photodetector combines ease of fabrication with significant performance improvements, expanding the potential applications of 4H SiC in high-temperature optoelectronics. It also introduces a promising pathway for enhancing 4H SiC-based photodetection capabilities across broader spectral ranges.

Speaker Identification is known as the technology that enables users to access a device by speaking into the microphone and detecting the present talker among a group of speakers. The deformation of the incoming voice signal by external sounds greatly degrades the quality of the speaker identification systems. Noise could impact the efficiency of speaker recognition as well as cause the system to not function appropriately. Therefore, speech enhancement is critical for improving the performance of speaker recognition systems under challenging conditions. The spectral Subtraction method is among the most common approaches offered for audio enhancement since it is simple to apply and requires minimal computation in signal processing. The suggested system combines three primary modules: background noise reduction, feature extraction, and sound classification. First of all, the speech enhancement approach has been used to remove the additive noise. Secondly, a combined strategy of feature extraction that uses Mel Frequency Cepstral Coefficients (MFCC) as a feature extractor to be integrated with a Mel filter bank as a single package has been used. Furthermore, by using extracted features and a deep learning algorithm, this method can recognize the identity of the speaker. A convolutional neural network (CNN) for speech modeling that demonstrates very positive results in classifying participants was applied. This architecture has been built in a text-independent configuration. The dataset was made containing 50 speakers, each speaker has 20 voice samples. The accuracy and precision parameters were used to check the feasibility of this model. Our successful hybrid approach attained an accuracy and precision of 98.46%.