Thermal Transport in Molecular Forests.

Heat propagation in quasi-one-dimensional materials (Q1DMs) often appears puzzling. For example, while an isolated Q1DM, such as a nanowire, a carbon nanotube, or a polymer, can exhibit a high thermal conductivity κ, forests of the same materials can show a reduction in κ. Until now, the complex structures of these assemblies have hindered the emergence of a clear molecular picture for this intriguing phenomenon. We combine coarse-grained simulations with concepts known from polymer physics and thermal transport to unveil a generic microscopic picture of κ reduction in molecular forests. We show that a delicate balance among the persistence length of the Q1DM, the segment orientations, and the flexural vibrations governs the reduction in κ.

Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.

Importance: The electronic health record (EHR) is a source of practitioner dissatisfaction in part because of challenges with information retrieval. To improve data accessibility, a better understanding of practitioners' information needs within individual patient records is needed. Objective: To assess EHR users' searches using data from a large integrated health care system. Design, Setting, and Participants: This retrospective cross-sectional analysis used EHR search data from Kaiser Permanente Northern California, an integrated health care delivery system with more than 4.4 million members. Users' EHR search activity data were obtained from April 1, 2018, to May 15, 2019. Main Outcomes and Measures: Search term frequency was grouped by user and practitioner types. Network analyses were performed of co-occurring search terms within a single search episode, and centrality measures for search terms (degree and betweenness centrality) were calculated. Results: A total of 12 313 047 search activities (including 4 328 330 searches and 7 984 717 result views) conducted by 34 735 unique users within 977 160 unique patient EHRs were identified. In aggregate, users searched for 208 374 unique search terms and conducted a median of 4 searches (interquartile range, 1-28 searches). Of all 97 367 active EHR users, 34 735 (35.7%) conducted at least 1 search. However, of all 12 968 active EHR physician users, 9801 (75.6%) conducted at least 1 search, and of all 1908 active pharmacist users, 1402 (73.5%) conducted at least 1 search. The top 3 most commonly searched terms were statin (75 017 searches [1.7%]), colonoscopy (73 545 [1.7%]), and pft (54 990 [1.3%]). However, wide variation in top searches were noted across practitioner groups. Terms searched most often with another term in a single linked search episode included statin, lisinopril, colonoscopy, gabapentin, and aspirin. Conclusions and Relevance: Although physicians and pharmacists were the most active users of EHR searches, search volume and frequently searched terms varied considerably by and within user role. Further customization of the EHR interface may help leverage users' search content and patterns to improve targeted information retrieval.

Effect of compression ratio, nozzle opening pressure, engine load, and butanol addition on nanoparticle emissions from a non-road diesel engine.

Currently, diesel engines are more preferred over gasoline engines due to their higher torque output and fuel economy. However, diesel engines confront major challenge of meeting the future stringent emission norms (especially soot particle emissions) while maintaining the same fuel economy. In this study, nanosize range soot particle emission characteristics of a stationary (non-road) diesel engine have been experimentally investigated. Experiments are conducted at a constant speed of 1500 rpm for three compression ratios and nozzle opening pressures at different engine loads. In-cylinder pressure history for 2000 consecutive engine cycles is recorded and averaged data is used for analysis of combustion characteristics. An electrical mobility-based fast particle sizer is used for analyzing particle size and mass distributions of engine exhaust particles at different test conditions. Soot particle distribution from 5 to 1000 nm was recorded. Results show that total particle concentration decreases with an increase in engine operating loads. Moreover, the addition of butanol in the diesel fuel leads to the reduction in soot particle concentration. Regression analysis was also conducted to derive a correlation between combustion parameters and particle number emissions for different compression ratios. Regression analysis shows a strong correlation between cylinder pressure-based combustion parameters and particle number emission.

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning.

Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nanosensors, we select the optimal set of illumination light-emitting diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nanohole arrays and revealed that the optimal illumination bands differ from those that are "intuitively" selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multispectral readers, helping the translation of nanosensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.