From weak to strong coupling: quasi-BIC metasurfaces for mid-infrared light-matter interactions.

Thanks to their giant, yet tunable, Q-factor resonances, all-dielectric metasurfaces supporting the quasi-bound states in the continuum (q-BIC) resonances are well-suited to provide a promising platform for quantum-coherent light-matter interactions. Yet, the strong coupling regime, characterized by the hybrid light-matter states - polaritons, has not yet been fully explored in the mid-infrared regime. This paper investigates the parameter space of vibrational strong coupling (VSC) between material and metasurface cavities supporting q-BIC resonances in the mid-infrared spectral range. We outline the effects of transition dipole strength, damping rate, and the number of molecules coupled to a single cavity, as well as the cavity damping rates, to understand their respective impacts on VSC. By tuning the Q-factor of the metasurface and material parameters, a new transition light-matter coupling zone is introduced, bridging the gap between weak and strong coupling, where polaritons form but their linewidths prohibit their spectral identification. The study further identifies the effects of cavity linewidth on polariton peak separability in strongly coupled systems, highlighting that the cavities with smaller nonradiative losses and narrower linewidths facilitate better polariton separability. Moreover, we found that matching cavity and material loss, satisfying the critical strong coupling condition, enhances the coupling strength between cavity and material. Overall, these findings can guide the design of photonic cavities suited for VSC experiments, contributing to the burgeoning fields of polaritonic chemistry, light-mediated modulation of chemical reactivity, and highly sensitive molecular spectroscopy.

Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.

Significance: Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues. Aim: To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures. Approach: We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment. Results: Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment. Conclusions: We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.

Multiplexed COVID-19 antibody quantification from human sera using label-free nanoplasmonic biosensors.

Serological assays that can reveal immune status against COVID-19 play a critical role in informing individual and public healthcare decisions. Currently, antibody tests are performed in central clinical laboratories, limiting broad access to diverse populations. Here we report a multiplexed and label-free nanoplasmonic biosensor that can be deployed for point-of-care antibody profiling. Our optical imaging-based approach can simultaneously quantify antigen-specific antibody response against SARS-CoV-2 spike and nucleocapsid proteins from 50 µL of human sera. To enhance the dynamic range, we employed multivariate data processing and multi-color imaging and achieved a quantification range of 0.1-100 µg/mL. We measured sera from a COVID-19 acute and convalescent (N = 24) patient cohort and negative controls (N = 5) and showed highly sensitive and specific past-infection diagnosis. Our results were benchmarked against an electrochemiluminescence assay and showed good concordance (R∼0.87). Our integrated nanoplasmonic biosensor has the potential to be used in epidemiological sero-profiling and vaccine studies.

Measurement of PET isotope production cross sections for protons and carbon ions on carbon and oxygen targets for applications in particle therapy range verification.

Measured cross sections for the production of the PET isotopes [Formula: see text], [Formula: see text] and [Formula: see text] from carbon and oxygen targets induced by protons (40-220 [Formula: see text]) and carbon ions (65-430 [Formula: see text]) are presented. These data were obtained via activation measurements of irradiated graphite and beryllium oxide targets using a set of three scintillators coupled by a coincidence logic. The measured cross sections are relevant for the PET particle range verification method where accurate predictions of the [Formula: see text] emitter distribution produced by therapeutic beams in the patient tissue are required. The presented dataset is useful for validation and optimization of the nuclear reaction models within Monte Carlo transport codes. For protons the agreement of a radiation transport calculation using the measured cross sections with a thick target PET measurement is demonstrated.