John T. W. Yeow

High-performance visible-light detection is vital for imaging and portable sensing, but most photodetectors are broadband intensity devices without intrinsic spectral selectivity and therefore rely on filter arrays or dispersive optics. Broadband visible photodetection with filter-free wavelength recognition is enabled by an inhomogeneous metasurface integrated with a convolutional Neural Network (CNN). Inhomogeneous metasurface creates geometry-dependent Localized Surface Plasmon Resonance (LSPR) hot spots that imprint wavelength-dependent signatures, while CNN is employed to recognize the signatures. The device shows broadband response in visible region with responsivity up to 1.45×10³ V W⁻¹, noise-equivalent power (NEP) down to 8.57 × 10 ^–16 W Hz ^1/2 , specific detectivity up to 5.71×10 ¹⁴ Jones. A lightweight 1D-CNN trained on a wavelength-distance dataset achieves 98.91% accuracy in 10-fold cross-validation and is validated by raster-scanning on a laptop display and traffic light recognition. The scalable Ag inhomogeneous metasurface/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) process and detector–CNN integration offer a practical route to low-cost, compact, filter-free colorful imaging and spectral identification.

In this work, we introduce a novel Micro Circular Log-Periodic Antenna (MCLPA) optimized with an advanced Evolutionary Neural Network (ENN) algorithm, specifically designed to enhance terahertz (THz) radiation detection. By leveraging the adaptive capabilities of the ENN framework, the antenna design efficiency is significantly improved, enabling rapid prototyping and yielding highly optimized structures tailored for practical THz applications. Extensive characterization confirms that the proposed MCLPA achieves outstanding performance, including an ultra-broad operational bandwidth of 372 GHz (0.135–0.507 THz), a peak gain of 5.51 dBi, an optimal S-parameter (S₁₁) of −13.68 dB, and a maximum radiation efficiency of 82.39%. In addition, the MCLPA exhibits superior sensitivity, low noise susceptibility, and fast response, which are key attributes for reliable and precise THz detection. When configured in array form, the design further enhances gain and directional responsiveness, demonstrating the scalability and deployment potential of the MCLPA. This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering, introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation. By substantially reducing design time and cost while achieving exceptional performance, the proposed ENN framework sets a new benchmark for the development of next-generation THz detection and communication systems, offering broad implications for future high-frequency technologies.

Field emission X-ray devices present numerous benefits compared to conventional X-ray sources, such as operating at lower temperatures, delivering enhanced spatial resolution, and consuming less power. These advantages are primarily due to the employment of field emission cathodes, which facilitate efficient electron emission at lower energy unlike thermionic sources. Owing to their superior electron emission properties, robust mechanical strength, and remarkable thermal stability, carbon nanotubes (CNTs) have emerged as a highly promising material for field emission cathodes. This review paper discusses the fundamental principles of X-ray generation, focusing on field emission-based structures. It highlights the superiority of CNTs in terms of stability, emission uniformity, and longevity, compared to conventional thermionic cathodes. The paper also explores advancements in CNT field emission arrays, detailing their integration into X-ray tubes, innovations in device design, and their applications in high-resolution imaging systems and medical diagnostics. Furthermore, the review addresses the challenges of scaling CNT emitters for commercial applications and their potential role in the next generation of X-ray technologies.

A novel flexible free-standing infrared graphene-based photothermoelectric detector has been designed and fabricated without any substrate support. The integration of polymers with nanomaterials has significantly enhanced the compatibility of multi-functional detection technologies. By combining graphene with poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS) polymer to form composites, the final photodetectors have performed high flexibility with bending treatments. The signal under blackbody infrared source radiation has been collected and compared across different graphene loadings, where 10 wt% loading of graphene achieved the highest photoresponsivity under peak wavelength at 4.3 micrometer within 6 seconds response time. It achieves maximum 2.27 $mV/W$ photoresponsivity with 6.95 $ \times {10^5}\;cm \cdot H{z^{1/2}}/W$ detectivity, which is 5 times higher than pure PEDOT: PSS-based detector. These findings provide valuable insights for advancing graphene-based composites in real-time infrared detection applications.