Molybdenum disulfide (MoS2), a two-dimensional material purchased from SPI supplies, was analyzed using high-resolution X-ray photoelectron spectroscopy (XPS). A flake was mechanically exfoliated from a MoS2 bulk single crystal for the study. The XPS spectra of MoS2 obtained using monochromatic Al Kα radiation at 0.83401 nm include a survey scan, high-resolution spectra of O 1s, Mo 3p, C 1s, Mo 3d, S 2s, S 2p, Mo 4s, Mo 4p, S 3s, and valance band. Extended energy ranges were collected in the vicinity of the Mo 3d/S 2s, S 2p, and Mo 4s/Mo 4p/S 3s/Valence band regions allowing for fitting of surface plasmon features and determination of Tougaard scattering cross-section parameters. Quantitative analysis indicates a surface composition of MoS1.9.

A novel optical method has been developed for the measurement of thermal accommodation coefficients in the temperature-jump regime. The temperature dependence of the resonant frequency of a fused-silica microresonator's whispering-gallery mode is used to measure the rate at which the microresonator comes into thermal equilibrium with the ambient gas. The thermal relaxation time is related to the thermal conductivity of the gas under some simplifying assumptions and measuring this time as a function of gas pressure determines the thermal accommodation coefficient. Using a low-power tunable diode laser of wavelength around 1570 nm to probe a microsphere's whispering-gallery mode through tapered-fiber coupling, we have measured the accommodation coefficients of air, helium, and nitrogen on fused silica at room temperature. In addition, by applying thin-film coatings to the microsphere's surface, we have demonstrated that accommodation coefficients can be measured for various gases on a wide range of modified surfaces using this method.

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We report an optical method for measuring the thickness of the water layer adsorbed onto the surface of a high-Q fused-silica microresonator. Light from a tunable diode laser operating near 1550 nm is coupled into the microresonator to excite whispering-gallery modes (WGMs). By observing thermal distortion or even bistability of the WGM resonances caused by absorption in the water layer, the contribution of that absorption to the total loss is determined. Thereby, the thickness of the water layer is found to be ∼0.1 nm (approximately one monolayer). This method is further extended to measure the desorption rate of the adsorbed water, which is roughly exponential with a decay time of ∼40 h when the fused-silica microresonator is held in a vacuum chamber at low pressure.

Understanding thermal transport in two- and three-dimensional periodic “holey” nanostructures is important for realizing applications of these structures in thermoelectrics, photonics and batteries. In terms of continuum heat diffusion physics, the effective medium theory provides the framework for obtaining the effective thermal conductivity of such structures. However, recently measured nanostructures possess thermal conductivities well below these continuum predictions. In some cases, their thermal conductivities are even lower than predictions that account for sub-continuum phonon transport. We analyze current understanding of thermal transport in such structures, discussing the various theories, the measurements and the insights gained from comparing the two.

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Background: Functional outcome scores provide valuable data, yet they can be burdensome to patients and require significant resources to administer. The Knee injury and Osteoarthritis Outcome Score (KOOS) is a knee-specific patient-reported outcome measure (PROM) and is validated for anterior cruciate ligament (ACL) reconstruction outcomes. The KOOS requires 42 questions in 5 subscales. We utilized a machine learning (ML) algorithm to determine whether the number of questions and the resultant burden to complete the survey can be lowered in a subset (activities of daily living; ADL) of KOOS, yet still provide identical data. Hypothesis: Fewer questions than the 17 currently provided are actually needed to predict KOOS ADL subscale scores with high accuracy. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Pre- and postoperative patient-reported KOOS ADL scores were obtained from the Surgical Outcome System (SOS) data registry for patients who had ACL reconstruction. Categorical Boosting (CatBoost) ML models were built to analyze each question and its value in predicting the patient’s actual functional outcome (ie, KOOS ADL score). A streamlined set of minimal essential questions were then identified. Results: The SOS registry contained 6185 patients who underwent ACL reconstruction. A total of 2525 patients between the age of 16 and 50 years had completed KOOS ADL scores presurgically and 3 months postoperatively. The data set consisted of 51.84% male patients and 48.16% female patients, with a mean age of 29 years. The CatBoost model predicted KOOS ADL scores with high accuracy when only 6 questions were asked ( R2 = 0.95), similar to when all 17 questions of the subscale were asked ( R2 = 0.99). Conclusion: ML algorithms successfully identified the essential questions in the KOOS ADL questionnaire. Only 35% (6/17) of KOOS ADL questions (descending stairs, ascending stairs, standing, walking on flat surface, putting on socks/stockings, and getting on/off toilet) are needed to predict KOOS ADL scores with high accuracy after ACL reconstruction. ML can be utilized successfully to streamline the burden of patient data collection. This, in turn, can potentially lead to improved patient reporting, increased compliance, and increased utilization of PROMs while still providing quality data.