Biological organisms are capable of generating inorganic materials to fulfil their needs in a process called biomineralization, and have been shown to exhibit control over the properties, such as size or shape, of the materials. Biomineralization occurs at mild conditions, which has generated a growing interest in replicating this process in a lab environment and developing methods that would allow researchers to apply this technique of biomimicry to the synthesis of inorganic materials that are not directly created through biomineralization. Biomimetic synthesis involves the use of biomolecules, such as proteins, DNA, or peptides, as templates or to induce mineralization. The mechanisms involved in synthesizing titanium dioxide, TiO2, using this technique of biomimicry are the specific interest of this project. Titanium dioxide is an oxide commonly found in titanium-containing ores, and has wide-spread application such as in pigments, pharmaceuticals, cosmetics, and photocatalysis. My work within this project specifically aims to understand the thermodynamics of the equilibrium equation between titanium(IV) bis(ammonium lactate)dihydroxide (TiBALDH), the preferred reagent for biomimetic titanium dioxide, and titanium dioxide. I created optimized structures of each reactant and product, and then I ran density functional theory (DFT) calculations using the computational chemistry software Gaussian to understand the energetics of each molecule. My use of these calculations to understand the energetics and mechanisms of this titanium dioxide reaction, as well as similar reactions, will help inform methods of improving the synthesis of titanium dioxide and other inorganic materials.

With growing concerns surrounding global warming, both pollution and alternative sources of energy have become the focus of intense research. These research efforts have addressed the need to power pollution sensors by alternative means. Harvesting energy from trees is both minimally invasive to the environment and eliminates harmful waste produced by non-green energy solutions like batteries. The intent of this research is to verify the possibility of developing an unconventional thermoelectric generator (TEG) in order to increase the energy obtained from trees. We believe that a comprehensive understanding of the temperature characteristics of trees improves the current thermoelectric harvester design. A more robust TEG produces the voltages necessary to power these IoT devices. The primary concerns in selecting materials for a specialized TEG are the thermal impedance, the dimensions and arrangement of semiconducting material, and the overall geometric composition of the TEG itself. The selection of these materials and the overall physical characteristics of the TEG depend on the analysis of the internal temperature of trees. We have developed and will soon deploy a system that captures this valuable data over time. Furthermore, we have established a theoretical TEG design tailored specifically to harvest energy from trees more effectively than previous implementations. The new harvester will be utilized to power LoRa wireless sensor networks capable of monitoring pollution and other environmental hazards. If successful, future research should be devoted to optimizing the TEG to minimize thermal resistances and parasitic heat losses, as well as efforts to maximize temperature differentials with the use of smart heat exchangers on the TEG’s ambient side. Electrical matching of the TEG and sensor node could be desirable too. Finally, integration of the TEG into other sensor applications such as wildfire monitoring is more than reasonable.

Metallic foams have long been sought after for their conductive and structural properties, but have not become widespread due to the extraordinarily difficult processes typically used to produce such materials. Utilizing conventional Fused Filament Fabrication (FFF), also known as Fused Deposition Modeling (FDM), 3D printing equipment and the viscous properties exhibited by the molten filament extruded during printing, we have established that these properties can produce fully customizable metallic foams. The material properties of these foams are configurable to produce varying degrees of density, scale, and geometry via the manipulation of standard printing variables such as print height, print speed, and extrusion speed. Preliminary results with polymeric FFF filaments have successfully produced foams demonstrating significant compressive strength in every direction despite being an open celled geometry with an extremely high surface area to volume ratio. Conversely, polymer foams printed with flexible materials allow us to tailor material properties for energy absorption over structural integrity. This work will establish the minimum and maximum limits of the fabrication process as well as the material properties of metallic foams. To date, our experiments have demonstrated this technique of manufacturing metallic foams is reliable, controllable and scalable with great potential to change the availability and usage of metallic foams, and could significantly impact fields such as structural engineering, automotive, and aerospace where these types of metallic foams are highly sought after.

All-electronic approaches, such as the Single Molecule Break Junction method (SMBJ), are emerging as promising alternatives to the traditional polymerase chain reaction based DNA sequencing. However, existing work on DNA sequence identification based on conductance histograms requires hundreds or thousands of measurements to obtain accurate identification results. Previously, our team demonstrated a machine learning (ML) based sequence identification system that requires only 20-30 current spectra to achieve accurate and real-time results. Broadly speaking, my work involves: (i) designing a classifier that is relatively robust to experimental noise, (ii) development of a classification methodology that is insensitive to the choice of bias voltage during SMBJ experiments and (iii) performance validation on a wide range of DNA samples. SMBJ experiments are inherently noisy, and molecular binding is not guaranteed on every experiment. Current spectra recorded from experiments without any molecular binding can be viewed as noise. Too many “noisy spectra” can significantly distort the conductance histograms when constructed from a relatively small number of experiments, ultimately impairing the performance of the identification system. I am currently developing an iterative clustering algorithm, which can automatically filter out the noisy spectra. The performance of this algorithm will be compared to traditional statistical tests currently used by the team. On another front, I have tested our current experimental framework on a more extensive database of DNA samples. My experiments revealed that the classifier does not work too well on certain types of DNA sequences, the reasons for which are not yet fully understood. We are currently performing simulations and molecular modeling to better understand the characteristics of DNA sequences which appear to be hard to classify within our ML framework. Insights derived from these physical models should allow us to better understand the DNA conductance mechanism and utilize DNA molecules to develop next-generation nano-devices.

Current research and development of robotic arms aims to increase both functionality and versatility. In the agriculture industry autonomous harvesting machines have the potential to be cost effective tools that efficiently pick crops, and robotic arms are a key component of that process. The agricultural robotics market was valued at $7.4 billion in 2020. Robotic arms available are typically designed to pick up one specific object. The market lacks solutions that can harvest a wide variety of crops quickly and carefully. The goal of this research is to develop an adaptable and robust grasping mechanism to attach to robotic arms for harvesting crops from a prototype developed for prostheses. This design adapts in position in response to the object geometry to reduce pressure on the object and requires less time to position the arm. Reducing pressure is a key metric in this study because of the fragile nature of many crops. Utilization of this mechanism reduces articulation time because it can adapt to the shape of the object being grasped at any orientation. My work in the lab has been to develop testing methods to prove these theories, both in simulation and through printing my own prototypes and performing physical tests. Preliminary virtual models and prototype tests consist of repeated grasp tests on a standard set of different grasp test objects including plastic fruits, cleaning supplies, and children’s toys. The objects are grasped repeatedly with and without the linkage mechanism attached. Results show that the mechanisms are adaptable and provide more contact area with the grasped object, reducing point pressure and requiring less articulation of the robotic arm. Further testing will apply the mechanisms in an agriculture setting and include prototyping with different materials and improved design.