Characterizing the Mechanisms of Laser-Induced Nucleation using Microfluidics Guided by Machine Learning

Information

  • NSF Award
  • 2410704
Owner
  • Award Id
    2410704
  • Award Effective Date
    9/1/2024 - 5 months ago
  • Award Expiration Date
    8/31/2027 - 2 years from now
  • Award Amount
    $ 530,132.00
  • Award Instrument
    Standard Grant

Characterizing the Mechanisms of Laser-Induced Nucleation using Microfluidics Guided by Machine Learning

Crystallizations are resource-intensive purification techniques in the manufacture of materials and chemicals that society uses every day. Nucleation is the birth of a crystal from a solution, in which molecules come together to form the tiny nucleus that eventually grows into a larger crystal. This nucleation process is critical to produce valuable products, such as dyes and pharmaceuticals. However, it requires significant amounts of chemical solvents and energy. This project seeks to improve the sustainability of crystallizations by pinpointing mechanisms by which light can induce nucleation. Using an external trigger, such as light, to induce and control nucleation has the potential to reduce the environmental impacts of crystallizations in industrial processes. The project will also develop basic design rules that may ultimately provide better control over crystal shape and the arrangement of molecules to properties that can be optimized for the particular application of the materials. The investigators will also create educational activities that train undergraduate and graduate students from diverse backgrounds to design “greener” crystallizations, making it an inherent part of basic chemical engineering education. By collaborating with the Applied Research Innovations in Science and Engineering (ARISE) program, the investigators will mentor underrepresented high-school students who will complete summer research experiences. <br/><br/>The overall mission of this research program will be to design computer-aided, high-throughput crystallization experiments to quantify the mechanisms that govern non-photochemical laser induced nucleation (NPLIN). Of specific interest will be the quantification of the conditions in which the dielectric polarization (DP) and colloidal impurity (CI) mechanisms govern light-induced nucleation. The novel computer-aided experimental methodology will examine three elementary crystallizations: i) urea as a model single-step nucleation, ii) glycine as a two-step nucleation, and iii) amino acid oligomers, such as glycylglycine and triglycine, that are building blocks for proteins. Continuous-flow, high-pressure microfluidic devices coupled to a laser will be designed and implemented to switch off the CI mechanism by suppressing the formation of nanobubbles. Supervised machine learning methods will be trained with data collected at ambient and elevated pressures to build design rules for the DP and CI mechanisms. Computer-aided experimental methods for the study of crystal nucleation mechanisms, a field that remains vastly an art and based on outdated batch techniques, is an emerging area of science that has the potential to create new unit operations in industrial processes.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

  • Program Officer
    Rohit Ramachandranrramacha@nsf.gov7032927258
  • Min Amd Letter Date
    8/2/2024 - 6 months ago
  • Max Amd Letter Date
    8/2/2024 - 6 months ago
  • ARRA Amount

Institutions

  • Name
    New York University
  • City
    NEW YORK
  • State
    NY
  • Country
    United States
  • Address
    70 WASHINGTON SQ S
  • Postal Code
    100121019
  • Phone Number
    2129982121

Investigators

  • First Name
    Ryan
  • Last Name
    Hartman
  • Email Address
    ryan.hartman@nyu.edu
  • Start Date
    8/2/2024 12:00:00 AM
  • First Name
    Bruce
  • Last Name
    Garetz
  • Email Address
    bgaretz@nyu.edu
  • Start Date
    8/2/2024 12:00:00 AM

Program Element

  • Text
    Proc Sys, Reac Eng & Mol Therm
  • Code
    140300