Utilizing the supportive structure of hydrogels, the semiconducting character of porous silicon (pSi) membranes, and the biodegradability of both, a unique biosensor for the chemical analysis of health-relevant analytes can ideally be created.
Alginate-based hydrogels are water-infused, biodegradable polymer networks. These are particularly useful because of their environmental abundance, and their ability to interface well with human skin. These characteristics also make them an ideal medium for supporting pSi membranes and simultaneously assimilating them into a wide range of tissues.
Porous silicon (pSi), a highly porous form of the elemental semiconductor, is utilized to measure and conduct electrical signals throughout the hydrogel matrix. In diode form, these membranes exhibit measurable current values as a function of voltage, which can be used to detect bioelectrical stimuli such as the concentration of physiologically relevant ionic species (e.g. Na+, K+, and Ca2+).
Recent experiments center on integrating pSi membranes into various aqueous environments and hydrogels to test how variations in ion concentration affect the flow of electrical current as a function of applied voltage. pSi membranes are fashioned into diodes upon the attachment of 0.25 mm diameter copper wire using silver epoxy and annealing. An electrochemical cell is created by placing two pSi membranes parallel each other in an electrolyte composition. Current is measured as a function of applied voltage (typically from 0-5 V) for systems with differing NaCl concentration.
As expected, the magnitude of maximum current response is proportional to ion concentration present in the electrolyte, with an order of magnitude amplification or more of measured current for a given voltage upon immersion of the electrodes in an alginate hydrogel matrix relative to water alone.
This presentation will focus on initial diode fabrication protocols, as well as establishing limits of detection for simple ions species present in human sweat. More refined strategies are also envisioned, including the development of methods for stabilization of sensor performance along with miniaturization of the sensing platform itself.

Light-driven reactions, such as those utilized in photoelectrosynthetic applications, focus on capturing and transferring light energy to drive chemical reactions. For this purpose, light-active metal oxide semiconductor materials are used, such as BiVO4, 𝛼-Fe2O3, and WO3 to list a few. Previous work demonstrated the use of BiVO4 electrodes to drive the oxidation of benzyl alcohol to benzaldehyde in the presence of a TEMPO (2,2,6,6-tetramethylpiperidine) mediator.1 This study seeks to improve the photoelectrochemical performance of this reaction by using a heterojunction WO3-BiVO4 electrode. We hypothesize that the heterojunction would decrease charge carrier recombination and improve the photochemical yield of the reaction compared to a BiVO4 electrode.2,3 The WO3-BiVO4 interface forms a type II band alignment allowing electrons from photoexcited BiVO4 to transfer into WO3 and holes to accumulate at the BiVO4-electrolyte interface.4 Two techniques, UV-visible spectroscopy and incident photon-to-current efficiency (IPCE) measurements, were applied to better understand why the heterojunction improved the photocurrent density in the presence of reaction components in solution. UV-visible spectroscopy was used to determine the band gaps of the materials. Information about the efficiency of light energy conversion to chemical energy was obtained by IPCE measurements. IPCE values are determined by relating the proportion of incident light power to the current produced by illuminating the WO3­-BiVO4 photoanode over a small wavelength range. Photoanodes exhibiting higher IPCE % are more effective at driving photoelectrosynthetic reactions.1 To test the effect of WO3 on the energy conversion efficiency, IPCE experiments were run for the WO3-only, BiVO4-only, and WO3-BiVO4 samples. Comparing IPCE values for WO3-BiVO4 samples shows a clear increase compared to BiVO4-only photoanodes. These results demonstrate how coupled materials (WO3-BiVO4) can generate higher current densities upon illumination for driving photoelectrosynthetic reactions.

Chalk Mountain Services of Texas, LLC. is a trucking company whose business is transporting raw materials, such as fracking sand, to various oilfield sites in and around west Texas. With over 1,300 assets in their fleet, they’re presented with a number of logistical problems, like optimizing a driver’s time to make as many trips between drill sites and raw material depots as possible in a day. Such routing and scheduling applications must have accurate data—the assets are either in or out of service and their location—to schedule sensible routes.

Should an asset break down in the unforgiving terrain of west Texas, the appropriate employee should have the ability to take note of such an incident so that routing and scheduling applications have correct, up-to-date data. The company’s current solution allows for any user to make changes to any asset, regardless of authorization status. Inconsistencies in assets’ statuses can lead to an employee having to manually intervene in the scheduling process, which decreases the company’s overall efficiency. Additionally, their current application is not mobile-friendly, but a sizable portion of users nevertheless interface with the current website from their phones.

The company’s expectations come in either one of two forms: a website and a companion app or a reactive website that can be used on a desktop or mobile device. The application shall use CRUD—create, read, update, and delete—methods to keep track of the assets, and the application shall provide different users with different access levels with Active Directory authentication. We have created a reactive website that can be used from either a desktop environment or mobile one, and our implementation of their requirements exists as a three layer architecture: a Microsoft SQL Server database, a backend developed in NodeJS, and a React front end. To make the deployment as simple as possible, we did not pursue developing the application on cloud providers; the application depends on a connection to an in-house SQL server and Active Directory service both of which cannot be accessed outside their intranet and are critical to the application’s functionality.

The Instructional Equity Observing Tool is an online video/audio analysis tool that is geared towards assisting the teachers and faculty of educational institutions in analyzing and understanding how their interaction with students translates into real learning. Our platform is meant to replace the current, manual method of analysis that many teachers/instructors perform to try and quantify different metrics about their teacher-student interaction. Instructors have expressed desire to view metrics such as the time the teacher talks during a lesson, what is the response time of students to those questions, and other data points such as the types of questions being asked (as categorized by Bloom’s Taxonomy). Quantifying these instructional variables helps these instructors more accurately understand the areas that they are strong in, and more importantly, the areas in which they can be more interactive with the students as to allow them to better absorb the lessons being taught. With the help of our tool, we can allow teachers to quickly and efficiently gather this data about each of their lessons so that data driven changes in teaching techniques is possible, and moreover, so that teachers can identify potential vectors of ineffective instruction.
The process for using this application is for a user to login/sign-up for our site, then they will proceed to upload either an audio or video file to the designated location. Our tool will then take that video/audio file and execute a customized API call to AssemblyAI (https://www.assemblyai.com/) that transcribes this file into text. We then perform specialized data manipulation operations on the transcript to generate all the different metrics and display them in an easy-to-read format that the user can then scroll through and analyze the results. The user will also have the option to save this report that is generated as a pdf, which they or an administrator role will be able to access and view again at a later time.
Our application is hosted using Amazon Web Services (AWS) and utilizes many different functionalities that this service provides. AWS manages our authentication and authorization, user account management, and report storage functionalities. Our current system does not use its own machine learning model and instead offloads transcription to the AssemblyAI API, however this could be updated in the future with the addition of large datasets for training. A specifically trained machine learning model in this case could provide a more accurate categorization of questions and a more flexible tool that could eventually make predictions or suggestions to the user on the best ways to improve their teaching methods.

Open Planner is a web application designed to meet the increasing need for college students to have a way to more easily organize and access major
assignment/exam dates across all courses during busy college semesters. Open Planner seeks to ease agenda making for students by parsing uploaded student syllabi for major assignment/exam dates and generate a personalized calendar the student can access from his/her account upon sign-up and syllabus upload. Once they have access to their personal calendar, students will be able to add events, delete and modify existing events, and customize their course calendars, giving them fast access to a customized and modifiable calendar without the time demanding task of looking through course syllabi and adding major dates one by one.