Inspired by the formulation of quantum-electrodynamical time-dependent density functional theory (QED-TDDFT) by Rubio and co-workers [Flick et al., ACS Photonics 6, 2757-2778 (2019)], we propose an implementation that uses dimensionless amplitudes for describing the photonic contributions to QED-TDDFT electron–photon eigenstates. This leads to a Hermitian QED-TDDFT coupling matrix that is expected to facilitate the future development of analytic derivatives. Through a Gaussian atomic basis implementation of the QED-TDDFT method, we examined the effect of dipole self-energy, rotating-wave approximation, and the Tamm–Dancoff approximation on the QED-TDDFT eigenstates of model compounds (ethene, formaldehyde, and benzaldehyde) in an optical cavity. We highlight, in the strong coupling regime, the role of higher-energy and off-resonance excited states with large transition dipole moments in the direction of the photonic field, which are automatically accounted for in our QED-TDDFT calculations and might substantially affect the energies and compositions of polaritons associated with lower-energy electronic states.

Following the formulation of cavity quantum-electrodynamical time-dependent density functional theory (cQED-TDDFT) models [Flick et al., ACS Photonics 6, 2757–2778 (2019) and Yang et al., J. Chem. Phys. 155, 064107 (2021)], here, we report the derivation and implementation of the analytic energy gradient for polaritonic states of a single photochrome within the cQED-TDDFT models. Such gradient evaluation is also applicable to a complex of explicitly specified photochromes or, with proper scaling, a set of parallel-oriented, identical-geometry, and non-interacting molecules in the microcavity.

The global trends of urbanization and industrialization have given rise to critical environmental and air pollution issues that often receive insufficient attention. Among the myriad pollution sources, volatile organic compounds (VOCs) stand out as a primary cluster, posing a significant threat to human society. Addressing VOCs emissions requires an effective mitigation action plan, placing technological development, especially in detection, at the forefront. Photonic sensing technologies rooted in the infrared (IR) light and matter interaction mechanism offer nondestructive, fast-response, sensitive, and selective chemical measurements, making them a promising solution for VOC detection. Recent strides in nanofabrication processes have facilitated the development of miniaturized photonic devices and thus sparked growing interest in the creation of low-cost, highly selective, sensitive, and fast-response IR optical sensors for VOC detection. This review work thus serves a timely need to provide the community a comprehensive understanding of the state of the art in this field and illuminate the path forward in addressing the pressing issue of VOC pollution.

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

We propose a photonic array of resonant circular dielectric waveguides with subwavelength grating that can be designed as a robust and sensitive topological chemical sensor. The device can detect trace amounts of a given chemical species through photonic edge modes that are impervious to most sources of disorder. We perform a simulation in the midinfrared that accounts for the absorption loss introduced by chemical molecules in contact with a strongly coupled photonic lattice of resonators. Due to the topological nature of the device, its chemical sensitivity scales linearly with the system size and can reach parts-per-billion range at the millimeter scale. Our findings suggest that topological chemical sensors could empower the development of novel on-chip integrated photonic sensing technologies.