Professor Robert Taylor

Abstract:

Lead-halide perovskite nanocrystals (NCs) have gained attention for optoelectronics, but careful selection of the antisolvent used for purification is essential to achieve high monodispersity and yield while minimizing surface damage. Current understanding indicates that this requires lowering the relative polarity of the antisolvent, yet high-polarity antisolvents are widely used for purification, as we confirm through data mining. We show that polarity alone is insufficient for antisolvent selection by comparing ethyl acetate and acetonitrile for CsPbI3 NC purification. Despite its higher polarity, acetonitrile yields improved colloidal stability compared to ethyl acetate. Using 1H NMR, FTIR, and XPS measurements, alongside DFT calculations, we demonstrate that acetonitrile acts as a stronger Lewis base, binding to and passivating the NC surface. Coordination of acetonitrile to the perovskite NC surface enhances stability and improves their performance in light-emitting diodes. These findings establish a mechanistic framework for antisolvent selection to realize bright and stable halide perovskite NCs.

Abstract:

Graphene nanoribbons (GNRs) combine the remarkable optical and electronic properties of graphene with the presence of a tunable band gap, making them promising for optoelectronic applications. Here, we investigate the excitonic properties of individual cove-edge GNRs through microphotoluminescence (micro-PL) spectroscopy. We observe ultranarrow emission lines with full width at half-maximum as low as 24 μeV, demonstrating a reduction of inhomogeneous broadening by 3 orders of magnitude compared to GNR ensembles. Temperature-dependent PL reveals phonon-mediated broadening mechanisms, with electron–phonon coupling parameters in agreement with ensemble studies but with dramatically reduced line widths. Time-resolved PL suggests long-lived excitonic states, while spectral diffusion analysis demonstrates stable emission energies, highlighting the exceptional quality of these GNRs as single-photon emitters. The absence of intensity blinking and low Mandel parameters further support the robustness of the emission properties. Our findings establish cove-edge GNRs as promising candidates for quantum light sources and nanoscale optoelectronic applications.

Abstract:

All-organic circularly polarized luminescence (CPL) emitters acting as intrinsic liquid polarizers provide a promising route to reduce optical crosstalk and enhance spatial resolution in displays by directly emitting circularly polarized light, thereby eliminating external polarizers and minimizing energy loss. Herein, we report a highly efficient, all-organic CPL-active liquid polarizer based on chiral organic binary composites (COBCs), in which camphorquinone-derived chiral inducers are integrated with polymeric carbon quantum dots (PCQDs), opening a previously unexplored pathway toward chiral organic-quantum dot composites. The composites exhibit intense blue emission with a photoluminescence quantum yield (PL QY) of 64%, and strong enantioselective CPL with luminescence dissymmetry factors (g_lum ≈ ±10^-2). Circular dichroism spectroscopy reveals multiple Cotton effects with high absorption anisotropy (g_abs = 1.2 × 10^-2), while time-resolved photoluminescence and electrochemical analyses indicate that hydrogen-bonded chiral networks promote charge transfer and generate intrinsic chiral fields enabling selective CPL emission. A prototype device based on COBCs achieves a spatial resolution of 4 lp mm^-1, nearly double that of achiral analogues, while effectively suppressing glare and enhancing image contrast. Our findings establish a design strategy for transforming achiral CQDs into CPL-active materials, opening pathways toward next-generation, energy-efficient photonic and display technologies.

Abstract:

Graphene quantum dots (GQDs) exhibit complex photoluminescence (PL) originating from intrinsic sp2 carbon domains, surface functional groups, and structural defects. Yet the spectral overlap among these emissive channels hinders clear identification of their recombination pathways. Here, we investigate multichannel PL dynamics of commercial GQDs using time‐resolved and cryogenic PL spectroscopy. PL spectra reveal three distinct peaks: Peak I (443 nm) from π–π* transitions, Peak II (520 nm) from surface‐dominated contribution functional states, and Peak III (583 nm) from pyrrolic N‐related defects. Time‐correlated single‐photon counting detects only a 460 nm emission linked to graphitic N traps, indicating that Peaks I–III decay faster than the nanosecond window. Ultrafast optical Kerr‐gate measurements further resolve distinct lifetimes for hydroxyl (<5 ps), carboxyl (5–10 ps), amine (20–30 ps), and carbonyl (40–80 ps) groups. The transient evolution displays cascade relaxation from deep to shallow traps, evidenced by a progressive blue‐shift of Peak II. Cryogenic PL shows stable emission of Peak I, whereas Peak III red‐shifts and broadens with temperature, revealing strong electron–phonon coupling and deep‐level trapping. These results clarify the multichannel emission mechanisms of GQDs and provide design principles for tuning their optical properties.

Abstract:

The terahertz (THz) frequency range has emerged as a promising spectral window for broad applications, including next-generation wireless communication, high-resolution imaging, and ultrafast spectroscopy. Among the essential components in these systems, amplitude modulators with high quality (Q) factors can provide sharp, selective frequency responses, which are key requirements for scalable and high-performance THz systems. However, designing high-Q THz modulators remains challenging, as conventional full-wave simulations are time-consuming and inefficient. In this study, we propose a deep learning-based inverse design framework tailored for THz metasurfaces composed of split-ring resonators (SRRs). The framework is built on a tandem neural network architecture that couples a forward model with an inverse network to retrieve structural parameters from desired spectral responses. To enhance physical feasibility and predictive stability, we introduce an autoencoder-based spectral projection method. Our model accurately reconstructs SRR geometries across a wide range of spectral targets by learning the underlying physical relationships. Notably, we demonstrate the inverse design of Fano resonant geometries characterized by high-Q factors and sharp asymmetric resonances, which are essential features for achieving deep modulation. By extending the tandem deep learning approach to the THz domain and incorporating an autoencoder-based spectral projection, our framework provides a scalable and efficient pathway for the rapid prototyping of tunable, high-Q THz devices and lays the foundation for artificial intelligence-driven design of advanced THz photonic components.