Silicon nanowire piezoresistor and its applications: a review.

Monocrystalline bulk silicon with doped impurities has been the widely preferred piezoresistive material for the last few decades to realize micro-electromechanical system (MEMS) sensors. However, there has been a growing interest among researchers in the recent past to explore other piezoresistive materials with varied advantages in order to realize ultra-miniature high-sensitivity sensors for area-constrained applications. Of the various alternative piezoresistive materials, silicon nanowires (SiNWs) are an attractive choice due to their benefits of nanometre range dimensions, giant piezoresistive coefficients, and compatibility with the integrated circuit fabrication processes. This review article elucidates the fundamentals of piezoresistance and its existence in various materials, including silicon. It comprehends the piezoresistance effect in SiNWs based on two different biasing techniques, viz., (i) ungated and (ii) gated SiNWs. In addition, it presents the application of piezoresistive SiNWs in MEMS-based pressure sensors, acceleration sensors, flow sensors, resonators, and strain gauges.

Advances in silicon nanowire applications in energy generation, storage, sensing, and electronics: a review.

Nanowire-based technological advancements thrive in various fields, including energy generation and storage, sensors, and electronics. Among the identified nanowires, silicon nanowires (SiNWs) attract much attention as they possess unique features, including high surface-to-volume ratio, high electron mobility, bio-compatibility, anti-reflection, and elasticity. They were tested in domains of energy generation (thermoelectric, photo-voltaic, photoelectrochemical), storage (lithium-ion battery (LIB) anodes, super capacitors), and sensing (bio-molecules, gas, light, etc). These nano-structures were found to improve the performance of the system in terms of efficiency, stability, sensitivity, selectivity, cost, rapidity, and reliability. This review article scans and summarizes the significant developments that occurred in the last decade concerning the application of SiNWs in the fields of thermoelectric, photovoltaic, and photoelectrochemical power generation, storage of energy using LIB anodes, biosensing, and disease diagnostics, gas and pH sensing, photodetection, physical sensing, and electronics. The functionalization of SiNWs with various nanomaterials and the formation of heterostructures for achieving improved characteristics are discussed. This article will be helpful to researchers in the field of nanotechnology about various possible applications and improvements that can be realized using SiNW.

Silver Nanowires in Stretchable Resistive Strain Sensors.

Silver nanowires (AgNWs), having excellent electrical conductivity, transparency, and flexibility in polymer composites, are reliable options for developing various sensors. As transparent conductive electrodes (TCEs), AgNWs are applied in optoelectronics, organic electronics, energy devices, and flexible electronics. In recent times, research groups across the globe have been concentrating on developing flexible and stretchable strain sensors with a specific focus on material combinations, fabrication methods, and performance characteristics. Such sensors are gaining attention in human motion monitoring, wearable electronics, advanced healthcare, human-machine interfaces, soft robotics, etc. AgNWs, as a conducting network, enhance the sensing characteristics of stretchable strain-sensing polymer composites. This review article presents the recent developments in resistive stretchable strain sensors with AgNWs as a single or additional filler material in substrates such as polydimethylsiloxane (PDMS), thermoplastic polyurethane (TPU), polyurethane (PU), and other substrates. The focus is on the material combinations, fabrication methods, working principles, specific applications, and performance metrics such as sensitivity, stretchability, durability, transparency, hysteresis, linearity, and additional features, including self-healing multifunctional capabilities.

Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19

Multiple efforts to model the epidemiology of SARS-CoV-2 have recently been launched in support of public health response at the national, state, and county levels. While the pandemic is global, the dynamics of this infectious disease varies with geography, local policies, and local variations in demographics. An underlying assumption of most infectious disease compartment modeling is that of a well mixed population at the resolution of the areas being modeled. The implicit need to model at fine spatial resolution is impeded by the quality of ground truth data for fine scale administrative subdivisions. To understand the trade-offs and benefits of such modeling as a function of scale, we compare the predictive performance of a SARS-CoV-2 modeling at the county, county cluster, and state level for the entire United States. Our results demonstrate that accurate prediction at the county level requires hyper-local modeling with county resolution. State level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. As an important use case, leveraging high resolution modeling with public health data and admissions data from Hillsborough County Florida, we performed weekly forecasts of both hospital admission and ICU bed demand for the county. The repeated forecasts between March and August 2020 were used to develop accurate resource allocation plans for Tampa General Hospital. 2010 MSC92-D30, 91-C20

On Machine Learning-Based Short-Term Adjustment of Epidemiological Projections of COVID-19 in US

Epidemiological models have provided valuable information for the outlook of COVID-19 pandemic and relative impact of different mitigation scenarios. However, more accurate forecasts are often needed at near term for planning and staffing. We present our early results from a systemic analysis of short-term adjustment of epidemiological modeling of COVID 19 pandemic in US during March-April 2020. Our analysis includes the importance of various types of features for short term adjustment of the predictions. In addition, we explore the potential of data augmentation to address the data limitation for an emerging pandemic. Following published literature, we employ data augmentation via clustering of regions and evaluate a number of clustering strategies to identify early patterns from the data. From our early analysis, we used CovidActNow as our underlying epidemiological model and found that the most impactful features for the one-day prediction horizon are population density, workers in commuting flow, number of deaths in the day prior to prediction date, and the autoregressive features of new COVID-19 cases from three previous dates of the prediction. Interestingly, we also found that counties clustered with New York County resulted in best preforming model with maximum of R2= 0.90 and minimum of R2= 0.85 for state-based and COVID-based clustering strategy, respectively.