Materials that passively cool themselves are of particular interest in our society, particularly with the rise of climate change. Since air conditioning actively requires energy to operate and no process is 100% efficient, air conditioning temporarily cools an internal environment at the expense of the world at large. As such, alternative cooling methods are needed that are less energy-intensive. Prior research has demonstrated that materials can be engineered such that they experience a net loss of heat to the cold of space. However, this effect requires a clear view of the sky and minimal view of the ground, conditions rarely met in urban environments. In this experiment, we characterize a directional emitter designed to exhibit high emittance in the 0-180° azimuthal angles about its normal axis, and high reflectance elsewhere. Our samples were tested with a thermal camera at different angles of incidence with respect to a cold background and the resulting temperatures were recorded.

In this project, we create a setup to measure the pressure profile of eight lithium-ion batteries simultaneously that can be charging at different protocols. We then collect cycling and pressure data and use this data to train a machine learning model that can be used for prediction of pressure profiles. We then seek to create a dynamic charging protocol using a PID control with our machine learning predictions in loop. We found that pressure has distinct behavior that can be used for many applications. Our preliminary results show that our machine learning model is accurate for the data we have but needs more data and more tuning to be accurate on a wider range. Our early PID control implementation shows that fast charging can be done with a smaller percentage of time causing lithium plating, but more tuning is needed to use this protocol for complete prevention of lithium plating. This work improves upon the slow constant current-constant voltage charging conventionally done. This will allow for increased charging rates, decreasing the required charging time. In addition, future experimentation will explore the use of machine learning predicted pressure profiles as a means of estimating battery lifetime. Differential pressure sensing, machine learning predictions, and PID control implementation will allow batteries to be charged faster and last longer.

With climate change on the rise and only so limited time to save the earth, sustainability is at the forefront of issues that we need to solve. A viable solution is converting to renewable energy especially solar power, however our current technology has many limitations that make it hard to harness these natural resources. Luminescent solar concentrators are one such device which concentrate sunlight and direct it to the solar cells. Though this technology has been around for a long time, LSC's are inefficient because a significant amount of sunlight is lost due to isotropic light emissions from the LSC’s. Hence having anisotropic light emission from the solar concentrator plays a major role in determining the effectiveness of the LSC’s and solar cells. This project proposes an idea of orienting light emission (anisotropic) from the LSC’s by forming self-assembled 2-D and 1-D perovskites lattices. This was achieved by non dimensionalizing the formed 3-D perovskite nanocubes to 2-D nano wires or 1-D nanowires (shrinking the dimensionality of the structures would lead to less scattering of the sunlight) through solvent dependent interactions of surface passivating ligands and manipulating environmental conditions such as temperature. Self-assembled superlattices perovskite structures lead to uniform monodisperse layers and optically stable nanocrystals which is required for large scale applications.

Liquid Crystal Elastomers (LCEs) are a unique type of soft material combining flexible polymer network and rod-like liquid crystals (LCs) that can withstand higher strain than classical elastomers due to the reorientation of LCs. With more applications of LCEs starting to be realized, it will be crucial to understand the fracture mechanics of LCEs so future engineers would be able to prevent possible failures. The purpose of this research is to understand the fracture mechanics of LCEs by investigating the strain and displacement fields and the director rotation around a crack tip. To achieve strain and displacement measurements, we fabricated main-chain monodomain LCEs films with a small edge-crack, and stretched parallel, perpendicular and oblique to the initial director with different angles. The Digital Image Correlation (DIC) method through the Ncorr program on MATLAB was utilized to track the displacement and strain distribution in the LCE samples. The rotation of the director was measured using the optical polariscope method. In general, we found the directors around the crack tip field rotate to be tangential to the crack surface, and the directors at remote regions realigned to the stretching direction. The overall strain and displacement fields match with the simulation where displacement concentration around the crack tip shifts for the specimens with different initial directors. Future work on fatigue cycle and internal imperfection of LCEs would have to be done to understand the fracture mechanics of LCE thoroughly.

A communication system, which consists of a transmitter and a receiver, models the process by which information is sent and received. The transmitted symbols that are generated by a transmitter go through a noisy channel and reach the receiver end. The receiver needs to estimate the transmitted symbols by their noisy version. Claude Shannon developed a theory that determines the maximum rate at which the receiver can reliably estimate the transmitted symbols based on the noise’s statistics. To achieve the maximum rate, the transmitted signals need to approximately follow an optimal probability distribution, which can be done through probabilistic shaping. One method for probabilistic shaping is using a distribution matcher that takes a sequence of bits equally likely to be ones and zeros and maps it bijectively to a new sequence of symbols with the desired probability distribution.There are two types of distribution matchers denoted as constant and multi-composition distribution matchers or CCDMs and MCDMs. We coded a CCDM and a MCDM, which is a union of CCDMs. Two different versions of the MCDM based on a high probability and typical set rule were constructed. We found that MCDMs outperformed CCDMs in both normalized Kullback–Leibler (KL) divergence, a measure of how well the desired distribution is met, and matching rate, meaning we can send more information using less bits. By applying MCDMs to channels, we can achieve higher transmission rates and better noise correction to increase the efficiency and speed of the internet and communication systems around the world.

Radiotherapy is a well-established technique for treating durable malignant cells. In breast radiotherapy, regions of the breast containing cancerous cells are exposed to x-rays to shrink and kill tumors. However, this treatment method remains unsatisfactory due to crude setups and poor localization techniques that prevent effective normal organ sparing. Overlapping and nearby healthy cells may be unintentionally damaged by radiotherapy in addition to the targeted cancer cells, which results in life-threatening acute and chronic toxicities in breast cancer patients after treatment. To control healthy organ sparing and provide a reproducible setup, this work experimentally develops a pneumatically actuated soft robot to safely isolate the breast from other organs for imaging and treatment using Ecoflex, a silicone elastomer with a low Young’s Modulus. We pneumatically actuate the soft robot by pumping air into a network of air channels embedded within the robot’s body, causing specific sections of walls to expand and press against the breast. This expansion fixes the breast in a treatable position as far away from the rest of the patient’s body as possible. Upon actuation, the thickness of the inner wall pressing against the breast is less than 250µm, which minimizes interference with imaging and unwanted radiation exposure. Each device costs less than 5 USD to manufacture, so it is practical to custom-fit the robot to each patient and dispose of it after treatment. This work demonstrates a promising future for soft robots in medical applications due to their lightweight, adaptable, reproducible, and inexpensive features.