Engineering Commons^™

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

Thermal Conductivity And Mechanical Properties Of Interlayer-Bonded Graphene Bilayers, Afnan Mostafa

Masters Theses

Graphene, an allotrope of carbon, has demonstrated exceptional mechanical, thermal, electronic, and optical properties. Complementary to such innate properties, structural modification through chemical functionalization or defect engineering can significantly enhance the properties and functionality of graphene and its derivatives. Hence, understanding structure-property relationships in graphene-based metamaterials has garnered much attention in recent years. In this thesis, we present molecular dynamics studies aimed at elucidating structure-property relationships that govern the thermomechanical response of interlayer-bonded graphene bilayers.

First, we present a systematic and thorough analysis of thermal transport in interlayer-bonded twisted bilayer graphene (IB-TBG). We find that the introduction of interlayer C-C …

Design And Fabrication Of A Trapped Ion Quantum Computing Testbed, Christopher A. Caron

Masters Theses

Here we present the design, assembly and successful ion trapping of a room-temperature ion trap system with a custom designed and fabricated surface electrode ion trap, which allows for rapid prototyping of novel trap designs such that new chips can be installed and reach UHV in under 2 days. The system has demonstrated success at trapping and maintaining both single ions and cold crystals of ions. We achieve this by fabricating our own custom surface Paul traps in the UMass Amherst cleanroom facilities, which are then argon ion milled, diced, mounted and wire bonded to an interposer which is placed …

Wast3d Potential, Andrew Larsen

Masters Theses

Waste is obsolete. Standard building industry practices are harmful to the environment. Non-traditional construction methods were examined as alternatives. Circular design logic was the guiding principle in material choice. Additive manufacturing is a proven modern method for building construction. Research on 3D printing case studies revealed that recycled plastic is a proven material and readily available. Removing plastic waste from the environment and sequestering it in architectural components gives the material a new purpose. The component of focus was a building block for a wall assembly. Inspiration was taken from the hexagonal Basalt rock formations found near volcanic fault lines. …

Fungi In Flux | Designing Regenerative Materials And Products With Mycelium, Arvind Bhallamudi

Masters Theses

As the world grapples with the escalating crisis of climate threats and environmental degradation, this research delves into the synergistic potential of design and biology, developing safe and sustainable materials for applications in prototyping, furniture and interior design. Harnessing the power of a unique organism - fungi, the study proposes an accessible, efficient, and resilient material resource system. It utilizes local waste streams and mycelium (the vegetative part of fungi) to grow functional structures. An experimental and small-scale protocol is modeled by testing bio-fabrication and bio-printing methods. The composites' performance qualities and characteristics are evaluated through mechanical testing and a …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …