Plasmonic sensors have been used for a wide-range of biological and chemical sensing applications. Emerging nano-fabrication 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 nano-sensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multi-spectral 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 nano-sensors, 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 nano-hole 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 multi-spectral readers, helping the translation of nano-sensing 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.

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.

Current and future urban flooding is influenced by changes in short-duration rainfall intensities. Conventional approaches to projecting rainfall extremes are based on precipitation projections taken from General Circulation Models (GCM) or Regional Climate Models (RCM). However, these and more complex and reliable climate simulations are not yet available for many locations around the world. In this work, we test an approach that projects future rainfall extremes by scaling the empirical relation between dew-point temperature and hourly rainfall and projected changes in dew-point temperature from the EC-Earth GCM. These projections are developed for the RCP 8.5 scenario and are applied to a case study in the Netherlands. The shift in intensity-duration-frequency (IDF) curves shows that a 100-year (hourly) rainfall event today could become a 73-year event (GCM), but could become as frequent as a 30-year (temperature-scaling) in the period 2071–2100. While more advanced methods can help to further constrain future changes in rainfall extremes, the temperature-scaling approach can be of use in practical applications in urban flood risk and design studies for locations where no high-resolution precipitation projections are available.

Prosthetic limbs and assistive devices require customization to effectively meet the needs of users. Despite the expense and hassle involved in procuring a prosthetic, 56% of people with limb loss end up abandoning their devices [1]. Acceptance of these devices is contingent on the comfort of the user, which depends heavily on the size, weight, and overall aesthetic of the device. As seen in numerous applications, parametric modeling can be utilized to produce medical devices that are specific to the patient’s needs. However, current 3D printed upper limb prosthetics use uniform scaling to fit the prostheses to different users. In this paper, we propose a parametric modeling method for designing prosthetic fingers. We show that a prosthetic finger designed using parametric modeling has a range of motion (ROM) (path of the finger tip) that closely aligns with the digit’s natural path. We also show that the ROM produced by a uniformly scaled prosthetic poorly matches the natural ROM of the finger. To test this, finger width and length measurements were collected from 50 adults between the ages of 18–30. It was determined that there is negligible correlation between the length and width of the index (D2) digit among the participants. Using both the highest and the lowest length to width ratio found among the participants, a prosthetic finger was designed using a parametric model and fabricated using additive manufacturing. The mechanical design of the prosthetic finger utilized a crossed four bar linkage mechanism and its ROM was determined by Freudenstein’s equations. By simulating the different paths of the fingers, we demonstrate that parametrically modeled fingers outperform uniformly scaled fingers at matching a natural digit’s path.