University of Maryland at Baltimore County Awards
- Project Name: Photophysical Properties of MOF-Immobilized Photosensitizers
Awarding Agency: US Army CCDC Chemical Biological Center
Project Summary:
In recent years many groups have been examining ways to incorporate photosensitizers in systematic ways into porous structures such as metal-organic frameworks. These photosensitizers have included organic linkers based on pyrene or porphyrins, metal complexes including Ru(bipy)3, and metals, including lanthanides, within the framework. Even so, fundamental studies in how different metals and organic linkers interact with one another based on their proximity are lacking. Singlet oxygen generation is of particular interest as it can be of great use in fine chemical synthesis, waste water treatment, cancer therapy, as insecticides or herbicides, and in the destruction of sulfur mustard.
Outcome:
The completed objectives of this project are: 1.The synthesis and characterization of these MOFs will be conducted by the CBR Filtration Branch at CBC, 2. the Measure Ground-State Absorption Spectra of MOF-immobilized Ru(bipy)32+ and NU-1000, 3. Determined the fluorescence quantum yield and kf for MOF photosensitizers, 4. Determined Triplet-Triplet Absorption Properties of MOF-immobilized Ru(bipy)32+ and NU-1000), 5. the Quenching of T1 by encapsulated molecular oxygen, and 6. Determined the quantum yield of singlet oxygen (O2(1∆g)) production. - Project Name: Lifelong Multitask Nanoparametric Learning
Awarding Agency: United States Army Edgewood Chemical Biological Center
Project Summary:
This project aims at markedly broadening the applicability of lifetime learning by accommodating the kernel-based nonparametric learning framework, which can capture significant nonlinear structure in the data. To alleviate the inherent complexity of nonparametric learning, which grows with the data size, a suite of practicable algorithms will be developed with provable performance and convergence guarantees and consists four (4) major components. First, a lifetime multitask nonparametric learning algorithm and its reduced complexity practical version will be developed for basic empirical risk minimization objectives. Second, lifetime multitask reinforcement learning algorithms will be developed in the nonparametric setting. Third, lifetime multitask multi-view learning algorithms will be developed, which can integrate multi-modal sensors. The fourth component is the theoretical performance and convergence analysis of the developed methods.
Outcome:
The research consisted of: 1. establishing the Project Management Plan, 2. the approach for the Lifetime Multitask Nonparametric Learning for Empirical Risk, 3. the Lifetime Multitask Nonparametric Reinforcement Learning, 4. the Lifetime Multitask Multiview Nonparametric Learning, 5. the Theoretical Analysis, and 6. the Project Reporting to include the algorithms, test results and analyses will be documented and reported. The software and datasets were also be provided. - Project Name: Science and Technology Research Partnership Program (STRP)
Awarding Agency: Department of Energy
Project Summary:
This multi-institutional research collaboration will develop technologies that address the Department of Energy's research foci to develop feedstock technology, biofuel catalyst production, algal systems, and test/evaluate the development of sustainable aviation fuels by producing convert domestic biomass and other municipal waste resources, including plastics, into low-carbon fuels, intermediary products, and bioproducts.
Outcome:
This program provided curriculum and in-depth training courses for Minority-Serving Institutions that yielded high participation, multiple conference presentations, multiple publications, and patent applications.
