Project Description:

During processing and manufacturing of multicomponent formulated products, several challenges predominate in materials relevant to food, pharmaceutical, agricultural, painting, consumer goods industries. This project focuses on studying the influence of the starting material properties have during formulation and processing as well as stability and function performance. That will ultimately affect the quality of the product that could be detrimental to the end-user, the customer and/or the patient.

The student involved in this project will be trained in using the various analytical tools used for the physical, chemical and surface characterization of various material systems used in food and pharmaceuticals. It will be an emphasis that the student understands that his or her contribution and commitment are critical to the project. For that, we will familiarize the student with the scope of the project, the importance of sample preparation, the certainty of data collection, the respect to instruments and lab practices. The student will be encouraged to treat the data and be creative in interpreting the results and propose explanations to the results. The student will have to do literature search based on the specific topic. A report and presentation will be requested.

Requirements:

Physical chemistry, chemistry, biology, statistics, overall engineering background is desired.

The student should be motivated, committed, responsible, team player, creative.

Project Description:

Modern machine learning algorithms, especially those based on Deep Learning (DL), have effectively automated the creation of mathematical models from data across various scientific and engineering domains. Despite their success, these algorithms are primarily designed for specific datasets, relying on unique neural architectures. While proficient at classifying and regressing within known distributions, they fail in terms of generalization beyond these confines, creativity, and continuous learning capabilities. In real-world applications, particularly in scientific contexts, the ability to hypothesize beyond existing datasets is crucial, as true discovery intrinsically extends beyond current knowledge. It is imperative to recognize that comprehending the real world goes beyond static datasets and reasoning, given the infinite array of situations, task variations, goals, and success metrics.

This proposal aims to overcome the dataset-driven supervised learning paradigm and embrace deductive models that can unify a vast array of multi-modal data, automatically identify and leverage relevant inductive biases, and creatively design and implement experiments to reduce the space of all possible hypotheses significantly.

Requirements:

Solid background in undergraduate-level probability, linear algebra, and machine learning. Strong coding skills in Python and deep learning packages such as Tensorflow and Pytorch.

Project Description:

Current detection methods for foodborne bacteria are labor-intensive and expensive. There is an urgent need for portable, rapid, sensitive screening and detection strategies for foodborne pathogens that can also distinguish between live and dead bacteria.

Through chemical and biochemical functionalization, manufacturing strategies, and validation experiments, this project will allow for the development of an affordable, multiplexed, and rapid screening sensor that is portable, sensitive, accurate, affordable, and repeatable. The devices will be validated on real samples in Purdue University facilities. Ultimately, this work will result in a real-time screening tool designed to benefit the entire food industry - from the smallest restaurant to the largest food packaging center.

The goal of this project is to put forward nanotechnology enabled approaches for low-cost multiplexed dual sensing device for the rapid, reliable, and field deployable rapid screening and viability assessment of whole-cell foodborne pathogens. This will be achieved by, first, designing and understanding dual readout device manufacturing via deposition of biological inks. Second, validating this detection and viability assessment platform for whole-cell pathogens in buffers and real food samples, and measuring parameters critical for achieving biosensing functionality. Such scalable, quantitative, and multiplexed test for both detection and viability assessment could have a lasting impact on food safety by catalyzing studies on emerging pathogens or underfunded hazards.

Requirements:

Basic chemistry laboratory experience. Major in Materials Science, Biomedical Engineering, Chemistry, Biochemistry or related field are encouraged to apply.

Project Description:

Additive manufacturing with short-fiber reinforced polymers has enabled printing non-structural geometries on the scale of multiple meters. However, structural material systems that include continuous carbon fiber are required to enable printing structural components. Although approaches for printing with continuous fiber exist, the printing rates are limited to less than a kilogram per hour due to the physics of polymer melting. Hence, this research project focuses on a novel method developed for printing with a hybrid of continuous and discontinuous fibers at rates similar to large-scale additive manufacturing (>100 kg/hr). During this project, the student will conduct simulation and experimental studies in the CAMRI system developed at Purdue.

Requirements:

The student should have completed the following courses or their equivalent:
- Statics/dynamics
- Strength of materials
- Thermodynamics/heat transfer
- Differential equations
- Industrial automation
- Control engineering
- Numerical methods/Finite element method

Other desired experience:
- Programming in Python, C++, and Matlab
- Hands-on experience building prototypes

Desired attitude
- Attention to detail, eagerness to learn, and flexibility to work in a team.

Project Description:

The objective of this project is twofold. One of the objectives is to define a process using additive manufacturing of composite materials through which the body of an autonomous underwater vehicle (AUV) can be manufactured to meet mission specifics, actuation needs, and payload requirements. A modular vehicle will be designed for printing in sections with fiber-reinforced polymers using the Composites Additive Manufacturing Research Instrument (CAMRI) system developed at Purdue. Further, a joining technology will be developed to provide structural integrity and water resistance. The second objective is to endow the system with motion capabilities through a control and navigation unit. This latter involves integrating a multiprocessor system with different sensors that can interface with the main computer through serial interfaces. The overall goal is to validate the navigation capabilities of the prototype and estimate its hydrodynamic properties through an experiment with a motion capture system at the dive well of Purdue.
This project is a collaboration between Dr. Nina Mahmoudian, Dr. Eduardo Barocio, and Dr. Jalil Chavez-Galaviz.

Requirements:

- Required Skills:
- Controls
- Statics and Mechanics of Materials
- Machine and CAD Design
- Programming: Python, C, Matlab
- Communication Protocols: UART
- Preferred Skills:
- FEA
- CNC Machining
- Commitment
- Teamwork
- Communication Protocols: Ethernet, SPI, IIC

Project Description:

The properties of materials are controlled by their microstructure. Most engineering materials have a complex microstructure that is usually identified by subjective analysis. In this project, we aim to develop and apply deep-learning based machine learning models to identify and quantify the microstructures in 2D and 3D image datasets. The student will learn and apply new algorithms developed in the PI's group and work with a postdoctoral fellow to push this project forward.

Requirements:

Hard working, dedicated, team player who is willing to learn. A background in materials engineering is strongly preferred.

Project Description:

A rotating detonation engine (RDE) is a high-efficiency engine where one or more high-pressure explosions or detonations continuously travel around a cylindrical channel. RDEs have the potential to be superior to gas turbines and rocket engines and provide propulsion to hypersonic speeds. The shock waves produced by detonation, which occur at a specific and predictable frequency, cause significant stresses and high temperatures in the channel. At these operational temperatures, there are advantages to fabricating this channel of a ceramic material. However, the operating conditions are challenging for brittle elastic materials. We propose the development of a new class of ceramics that intentionally has discrete viscous phases added to a purely elastic matrix. This approach will take advantage of the energy-absorbing relaxation mechanisms associated with glassy phases near their Tg, primarily via translational and rotational motions of the silica tetrahedron and atoms. The student involved in this project will work on fabricating ceramic glass particles with tailored Tg properties. These glassy particles will be incorporated into other high-temperature ceramics, such as silicon nitrate (Si3O4). The student will learn about particle fabrication and characterization, ceramic processing, and material property measurements. Students with a background in materials science and engineering, mechanical engineering, and aeronautical engineering are well-suited to participate in the project.

Requirements:

Junior or senior in materials or mechanical engineering. Knowledge about materials processing and characterization is desirable.