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Dutcher Group

dutcher group at APS 2024

About Us

We use a broad range of experimental techniques to study the fundamental soft matter and biological physics of materials and systems with real world applications. Soft and biological systems are very sensitive to their environment such that small changes in temperature and pH, as well as the application of external fields, can produce large changes in their properties. This sensitivity can be exploited to tune the properties of the systems and to achieve a deep understanding of the subtle interplay between different interactions.

Our approach allows us to address fundamental issues ranging from the colloidal glass transition to nanoconfinement of water to diffusion in semi-crystalline polymers. Our work also contributes to solving problems that are important to society such as reducing the environmental impact of technologies and improving the sustainability of materials.

Work with us

This website highlights our research projects, facilities and equipment, research opportunities for graduate students and postdoctoral fellows, and research collaborations with industry. If you have any questions or would like to receive additional information, please feel free to contact Professor John Dutcher.

NameRole
John DutcherFaculty
Rob WickahmAssociated Faculty
Mike Grossuttii Research Assistant
Ben BaylisPostdoctoral Scholar
Carly MikiPhD Candidate
Benjamin MorlingPhD Candidate
Nicholas van HeijstPhD Candidate
Zach EvansMSc Candidate
Nishel AlexanderMSc Candidate
Ricky SummerlinMSc Candidate
Emma GreenallUndergraduate Research Student
Isaac MercierUndergraduate Research Student

Available Positions

The Dutcher Lab at the University of Guelph is seeking qualified MSc and PhD candidates to work on the application of machine learning (ML) and artificial intelligence (AI) to the analysis of large databases of infrared (IR) spectra collected in IR microscopy images of polymers. The goal in this work is to ultimately understand the degradation and failure mechanisms of polymers used in water transport applications, in collaboration with our industrial partner HeatLink.

We are looking for applicants who are excited to contribute to the forefront of the application of ML and AI strategies to the analysis of large databases, an emerging area at the intersection of physical and data science. Our recent use of a β-variational autoencoder (β-VAE) approach is particularly promising [1-3]. In this neural network-based approach, a very large number of IR spectra are used to train an encoder that forces the input spectra through an information bottleneck. By doing this, we can identify a small number of important generative factors called latent dimensions that are responsible for most of the measured variance in the dataset. New spectra from high resolution IR images collected on our in-house, state-of-the-art Bruker LUMOS II infrared microscope can then be analyzed using the β-VAE model to classify and track the spatial distribution of different modes of degradation in the polymers and identify new features in the data. Further insights can be achieved by using dimensionally reduced features, learned by β-VAE and other approaches, as inputs into clustering (k-means, hierarchical, and density based) and classification (support vector machines, k-nearest neighbours, and logistic regression) models.

HeatLink: https://www.heatlink.com

[1] M. Grossutti, J. D’Amico, J. Quintal, H. MacFarlane, A. Quirk and J.R. Dutcher. Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder. J. Phys. Chem. Lett. 13, 5787 (2022).
[2] M. Grossutti, J. D’Amico, J. Quintal, H. MacFarlane, W.C. Wareham, A. Quirk and J.R. Dutcher. Deep Generative Modeling of Infrared Images Provides Signature of Cracking in Cross-Linked Polyethylene Pipe. ACS Appl. Mater. Interfaces 15, 22532 (2023).
[3] J. D’Amico, M. Grossutti and J.R. Dutcher, Deep Learning Analysis of the Propagation of Stabilizing Additive Hydrolysis in a Cross-Linked Polyethylene Pipe. ACS Appl. Polym. Mater. 6, 534 (2024).

Position Requirements and Expectations

  • Completed or close to completing a Bachelors or Masters degree in physics, physical chemistry or a related field of physical science
  • Interest and strong motivation to work at the forefront of the application of machine learning techniques to physical science data
  • Strong analytical skills and the ability to think critically and creatively
  • Strong problem-solving skills and work ethic
  • Excellent hands-on laboratory skills including the use of advanced instrumentation
  • Ability to work safely and responsibly in a laboratory
  • Ability to apply sophisticated data analysis techniques to experimental data
  • Ability to program in Python and work with large databases
  • Ability to work effectively in a team environment
  • Strong oral and written communication skills
     

Start Date

The anticipated start date is in Fall 2025.
 

Application Process

Interested applicants should send a cover letter, CV and the names of up to three referees to (dutcher@uoguelph.ca). In your cover letter, you should highlight your relevant previous experience and training. Review of applications will begin immediately and continue until all positions are filled. Only applicants selected for an interview will be contacted. The Dutcher Lab and the University of Guelph are committed to building a diverse and inclusive community. All qualified applicants are invited to apply, but we particularly welcome applications from individuals that identify with groups traditionally underrepresented in the physical sciences, and we will strive to hire individuals who share our commitment to equity, diversity and inclusion.

The Dutcher Lab at the University of Guelph is seeking qualified MSc and PhD candidates to work on the characterization of new nanomaterials based on phytoglycogen (PG), a highly branched glucose polymer produced as compact, soft, hairy nanoparticles in the kernels of sweet corn. Not only are PG nanoparticles useful for applications in personal care and biomedicine, but they also provide an ideal system for studying the physics of soft nanoparticles. The Dutcher Lab uses a wide variety of techniques to characterize the structure, morphology, hydration and mechanical properties of PG nanoparticles, and our data show dramatic changes to the particle properties with simple modifications to the particles. One of the important measurements is called rheology, in which the mechanical properties of aqueous dispersions of PG nanoparticles are measured as a function of particle concentration [1,2]. At high concentrations, in which the particles are forced into contact, these measurements reveal the nature of the interaction between PG nanoparticles and, more generally, provide insight into the nature of the soft colloidal glass transition. Recently, we have shown that partially digesting PG particles using dilute acids produces smaller, less dense particles and significantly changes the interactions between the particles at high concentrations so that, surprisingly, the soft colloidal glass transition can be studied on experimental timescales [2].

We are looking for applicants who are excited to contribute to the forefront of investigating novel properties of soft nanoparticles. This work will involve performing simple chemical and physical modifications to PG nanoparticles, such as attaching chemical groups to the outer surface of the particles that add charge and/or hydrophobicity, and then measuring the mechanical properties of aqueous dispersions of the modified PG nanoparticles using a state-of-the-art rheometer. These data will be used together with data from other techniques such as atomic force microscopy, multi-angle light scattering and advanced computer simulations to achieve an understanding of how the interactions between PG particles change with modifications of the particles. This work should lead to new applications of natural, safe, sustainable PG nanoparticles.

[1] H. Shamana et al., Soft Matter 14, 6496 (2018).
[2] H. Shamana and J.R. Dutcher, Biomacromolecules 23, 2040 (2022).

Position Requirements and Expectations

  • Completed or close to completing a Bachelors or Masters degree in physics, physical chemistry or a related field of physical science
  • Interest and strong motivation to work at the forefront of the physics of soft nanoparticles
  • Strong analytical skills and the ability to think critically and creatively
  • Strong problem-solving skills and work ethic
  • Excellent hands-on laboratory skills including the use of advanced instrumentation
  • Ability to work safely and responsibly in a laboratory
  • Ability to apply sophisticated data analysis techniques to experimental data
  • Ability to program in Python and work with large databases
  • Ability to work effectively in a team environment
  • Strong oral and written communication skills
     

Start Date

The anticipated start date is in Fall 2025.
 

Application Process

Interested applicants should send a cover letter, CV and the names of up to three referees to (dutcher@uoguelph.ca). In your cover letter, you should highlight your relevant previous experience and training. Review of applications will begin immediately and continue until all positions are filled. Only applicants selected for an interview will be contacted. The Dutcher Lab and the University of Guelph are committed to building a diverse and inclusive community. All qualified applicants are invited to apply, but we particularly welcome applications from individuals that identify with groups traditionally underrepresented in the physical sciences, and we will strive to hire individuals who share our commitment to equity, diversity and inclusion.

Research Overview

Research Projects

We have two major research projects that involve: 

  1. a novel sustainable nanoparticle called phytoglycogen, and 
  2. the application of machine learning techniques to analyzing infrared microscopy measurements of cross-linked polyethylene pipe. 

In addition, we have an active collaboration on the production and characterization of plant-based “meats” with a texture that approximates that of a fibrous meat-like steak, as well as collaborations to use atomic force microscopy to study biological cells and hydrogels.

We study a novel polysaccharide called phytoglycogen that is produced as dense, compact nanoparticles in the kernels of sweet corn. The natural polymer particles have a special dendritic or tree-like architecture that imparts special properties. Because of their unique physical properties as well as their biocompatibility, non-toxicity and digestibility, the particles have distinct advantages for use in applications involving the human body, such as personal care, nutrition, and biomedicine.

To study the properties of these nanoparticles, we use a wide range of experimental techniques that includes small angle neutron scattering (SANS), atomic force microscopy (AFM), infrared (IR) spectroscopy, ellipsometry, rheology, dynamic light scattering (DLS), and size exclusion chromatography-multiangle light scattering (SEC-MALS). The results of our measurements of the structure, morphology, hydration, and mechanical properties have shown that native phytoglycogen nanoparticles are soft, hairy, porous, and hydrated. We also use computational techniques such as dynamic self-consistent field theory to model native and modified phytoglycogen nanoparticles and their interaction with other small molecules.  

Important things that we have learned:

We can also modify phytoglycogen nanoparticles in several useful ways: 

In our current research, we are investigating new ways to modify phytoglycogen nanoparticles to produce novel properties and identify new applications, and we are developing computer simulations of phytoglycogen nanoparticles using the technique of dynamic self-consistent field theory. This work involves collaborations with Prof. Rob Wickham (Guelph), Prof. Jon Nickels (Cincinnati), Dr. John Katsaras (Oak Ridge National Laboratory) and Prof. Mario Martinez (Aarhus). 

This safe, natural nanotechnology is being commercialized by our spinoff company, Mirexus Biotechnologies, which is working with customers to develop innovative products for personal care applications. 

Machine learning is revolutionizing the analysis of large databases. We are contributing to this revolution through our application of deep learning to the analysis of infrared spectra. 

We are studying cross-linked polyethylene (PEX-a) pipe, which you likely have as the water lines in your house. Commercial pipe formulations include additives that offer enhanced protection against degradation processes such as oxidation and UV-degradation. We use infrared (IR) microscopy to measure local changes to the polyethylene and the additives with in-service use at elevated temperature and pressure.

We place the pipes in an in-house water recirculation system that allows us to produce accelerated ageing of the pipes at different temperatures and environmental conditions. Aggressive ageing of the pipes can produce cracks in the pipes that ultimately leads to pipe failure. Our work is focused on understanding the formation and growth of cracks so that the lifetime of PEX-a pipe can be extended. 

Important things that we have learned:

Other Projects

We are collaborating with Prof. Mario Martinez of Aarhus University in Denmark to produce and characterize plant-based meat analogs. 

We are also collaborating with several biology groups at the University of Guelph to use atomic force microscopy to study biological cells and hydrogels. 

Industry

The Dutcher lab has several exciting collaborations with companies who are interested in understanding the properties of polymers and biopolymers for use in different applications. 

Funding Sources

We have funding from a variety of sources, including the Discovery Grant and Collaborative Research and Development programs of the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Ontario Research Fund, the HeatLink Group, and Mirexus Biotechnologies. 

In the past, we have received funding from the Canada Research Chairs program, the Engage program of the Natural Sciences and Engineering Research Council of Canada, the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), the Advanced Foods and Materials Network, the Ontario Centres of Excellence, Iogen Corporation, 3M Canada, Dow Chemical, Materials and Manufacturing Ontario, the Premier’s Research Excellence Award program, and the Ontario Innovation Trust.

Publications

Equipment

We use a wide range of state-of-the-art equipment in recently renovated laboratories to probe the structure, dynamics and mechanical properties of polymers, biopolymers and nanoparticles. In our group, we have Characterization Tools, Sample Preparation Facilities, and Polysaccharide Extraction and Purification Facility, as well as access to other tools within the Electrochemical Technology Centre, the Advanced Analysis Centre, and the Nanoscience Laboratory.  

  • Atomic force microscopes
  • Self-nulling ellipsometer
  • Rheometer
  • Differential scanning calorimeter
  • Thermogravimetric analyzer
  • SEC-MALS system
  • Surface plasmon resonance imaging system
  • Optical microscopes
  • Dynamic light scattering spectrometer
  • Attenuated total reflection FTIR spectrometer
  • Accelerated ageing recirculation system
  • UV-Vis-NIR Spectrophotometer
  • Nanodrop Spectrophotometer
  • Refractometer
  • Contact angle instrument
  • Infrared microscope

  • Spincoater
  • Glove box
  • Laminar flow cabinet
  • Ultrapure water facility
  • Vacuum ovens
  • UV/ozone cleaner
  • Plasma cleaner
  • CO2
  • snow cleaner
  • Microtome
  • High temperature oven
  • Langmuir trough
  • Microbiology facilities

  • Ultrafiltration system
  • Vacuum oven
  • Centrifuge
  • Spray dryer
  • Lyophilizer