Dark State Concentration Dependent Emission and Dynamics of CdSe Nanoplatelet Exciton-Polaritons.

The recent surge of interest in polaritons has prompted fundamental questions about the role of dark states in strong light-matter coupling phenomena. Here, we systematically vary the relative number of dark states by controlling the number of stacked CdSe nanoplatelets confined in a Fabry-Pérot cavity. We find the emission spectrum to change significantly with an increasing number of nanoplatelets, with a gradual shift of the dominant emission intensity from the lower polariton branch to a manifold of dark states. Through accompanying calculations based on a kinetic model, this shift is rationalized by an entropic trapping of excitations by the dark state manifold, while a weak dark state dispersion due to local disorder explains their nonzero emission. Our results point toward the relevance of the dark state concentration to the optical and dynamical properties of cavity-embedded quantum emitters with ramifications for Bose-Einstein condensate formation, polariton lasing, polariton-based quantum transduction schemes, and polariton chemistry.

Predicting sequenced dental treatment plans from electronic dental records using deep learning.

BACKGROUND: Designing appropriate clinical dental treatment plans is an urgent need because a growing number of dental patients are suffering from partial edentulism with the population getting older. OBJECTIVES: The aim of this study is to predict sequential treatment plans from electronic dental records. METHODS: We construct a clinical decision support model, MultiTP, explores the unique topology of teeth information and the variation of complicated treatments, integrates deep learning models (convolutional neural network and recurrent neural network) adaptively, and embeds the attention mechanism to produce optimal treatment plans. RESULTS: MultiTP shows its promising performance with an AUC of 0.9079 and an F score of 0.8472 over five treatment plans. The interpretability analysis also indicates its capability in mining clinical knowledge from the textual data. CONCLUSIONS: MultiTP's novel problem formulation, neural network framework, and interpretability analysis techniques allow for broad applications of deep learning in dental healthcare, providing valuable support for predicting dental treatment plans in the clinic and benefiting dental patients. CLINICAL IMPLICATIONS: The MultiTP is an efficient tool that can be implemented in clinical practice and integrated into the existing EDR system. By predicting treatment plans for partial edentulism, the model will help dentists improve their clinical decisions.

Control of Photoswitching Kinetics with Strong Light-Matter Coupling in a Cavity.

Most photochemistry occurs in the regime of weak light-matter coupling, in which a molecule absorbs a photon and then performs photochemistry from its excited state. In the strong coupling regime, enhanced light-matter interactions between an optical field and multiple molecules lead to collective hybrid light-matter states called polaritons. This strong coupling leads to fundamental changes in the nature of the excited states including multi-molecule delocalized excitations, modified potential energy surfaces, and dramatically altered energy levels relative to non-coupled molecules. The effect of strong light-matter coupling on covalent photochemistry has not been well explored. Photoswitches undergo reversible intramolecular photoreactions that can be readily monitored spectroscopically. In this work, we study the effect of strong light-matter coupling on the kinetics of photoswitching within optical cavities. Reproducing prior experiments, photoswitching of spiropyran/merocyanine photoswitches is decelerated in a cavity. Fulgide photoswitches, however, show the opposite effect, with strong coupling accelerating photoswitching. While modified merocyanine switching can be explained by changes in radiative decay rates or the amount of light in the cavity, modified fulgide switching kinetics suggest direct changes to excited-state reaction kinetics.

Direct Patterning of Optoelectronic Nanostructures Using Encapsulated Layered Transition Metal Dichalcogenides.

Direct top-down nanopatterning of semiconductors is a powerful tool for engineering properties of optoelectronic devices. Translating this approach to two-dimensional semiconductors such as monolayer transition metal dichalcogenides (TMDs) is challenging because of both the small scales required for confinement and the degradation of electronic and optical properties caused by high-energy and high-dose electron radiation used for high-resolution top-down direct electron beam patterning. We show that encapsulating a TMD monolayer with hexagonal boron nitride preserves the narrow exciton linewidths and emission intensity typical in such heterostructures after electron beam lithography, allowing direct patterning of functional optical monolayer nanostructures on scales of a few tens of nanometers. We leverage this fabrication method to study size-dependent effects on nanodot arrays of MoS2 and MoSe2 as well as laterally confined electrical transport devices, demonstrating the potential of top-down lithography for nanoscale TMD optoelectronics.

Spatial Mapping of Electrostatic Fields in 2D Heterostructures.

In situ electron microscopy is an effective tool for understanding the mechanisms driving novel phenomena in 2D structures. However, due to practical challenges, it is difficult to address these technologically relevant 2D heterostructures with electron microscopy. Here, we use the differential phase contrast (DPC) imaging technique to build a methodology for probing local electrostatic fields during electrical operation with nanoscale spatial resolution in such materials. We find that, by combining a traditional DPC setup with a high-pass filter, we can largely eliminate electric fluctuations emanating from short-range atomic potentials. Using a method based on this filtering algorithm, a priori electric field expectations can be directly compared with experimentally derived values to readily identify inhomogeneities and potentially problematic regions. We use this platform to analyze the electric field and charge density distribution across layers of hBN and MoS2.

Optimizing photonic ring-resonator filters for OH-suppressed near-infrared astronomy.

Near-infrared wavelength observations are crucial for understanding numerous fields of astrophysics, such as supernova cosmology and positronium annihilation detection. However, current ground-based observations suffer from an enormous background due to OH emission in the upper atmosphere. One promising way to solve this problem is to use ring-resonator filters to suppress OH emission lines. In this work, we discuss our optimization of ring-resonator filter performance from five perspectives: resonance wavelength matching, polarization-independent operation, low insertion loss, low-loss coupling to astronomical instruments, and broadband operation. In the end, we discuss next steps needed for reliable supernova and positronium observations, thus providing a roadmap for future advances in near-infrared astronomy.

Clinical decision support model for tooth extraction therapy derived from electronic dental records.

STATEMENT OF PROBLEM: Tooth extraction therapy serves as a key initial step in many prosthodontic treatment plans. Dentists must make an appropriate decision on the tooth extraction therapy considering multiple determinants and whether a clinical decision support (CDS) model might help. PURPOSE: The purpose of this retrospective records study was to construct a CDS model to predict tooth extraction therapy in clinical situations by using electronic dental records (EDRs). MATERIAL AND METHODS: The cohort involved 4135 deidentified EDRs of 3559 patients from the database of a prosthodontics department. Knowledge-based algorithms were first proposed to convert raw data from EDRs into structured data for feature extraction. Redundant features were filtered by a recursive feature-elimination method. The tooth extraction problem was then modeled alternatively as a binary or triple classification problem to be solved by 5 machine learning algorithms. Five machine learning algorithms within each model were compared, as well as the efficiency between 2 models. In addition, the proposed CDS was verified by 2 prosthodontists. RESULTS: The triple classification model outperformed the binary model with the F1 score of the Extreme Gradient Boost (XGBoost) algorithm as 0.856 and 0.847, respectively. The XGBoost outperformed the other 4 algorithms. The accuracy, precision, and recall of the XGBoost algorithm were 0.962, 0.865, and 0.830 in the binary classification and 0.924, 0.879, and 0.836 in the triple classification, respectively. The performance of the 2 prosthodontists was inferior to the models. CONCLUSIONS: The CDS model for tooth extraction therapy achieved high performance in terms of decision-making derived from EDRs.