On February 15, UNM Rainforest Innovations hosted a technology showcase to highlight physical science and engineering technologies developed at the University of New Mexico. There were five technology presentations by UNM faculty and all technologies discussed are currently available for commercialization.
The webinar kicked off with opening remarks by Lisa Kuuttila, CEO & Chief Economic Development Officer of UNM Rainforest Innovations followed by guest speaker Christos Christodoulou, Dean of Engineering and Computing at the University of New Mexico. All presentations were moderated by Alex Roerick, Senior Innovation Associate for Engineering & Physical Sciences.
Below are summaries of the technologies featured in the event and a recording of each presentation.
Multispectrally Engineered Microgeode Paints for Building Cooling (Ref. 2022-010)
Sang Eon Han, Ph.D., Associate Professor & Associate Chair, Department of Chemical & Biological Engineering
Researchers at the University of New Mexico, Harvard University and the Georgia Institute of Technology have collaborated to propose hierarchically structured materials based on colloidal nanowire geodes. Nanowire geodes are hollow colloidal particles with semiconductor nanowires decorating their interior, and whose composition and structure are “programmed” with nanoscale precision to yield a desired optical response. Geodes combine the many optical properties of nanowires with the processability of conventional colloidal particles, and constitute a versatile photonic materials platform for a range of applications. Their hierarchical structure can be controlled at each length scale, which is critical to achieving the goal of programmability over multiple spectral bands. Based on the above characteristics, micro-geode paints offer a combination of cooling power, ease of deployment, manufacturing scalability, aesthetics, and operational durability that does not exist today. Learn about the technology here.
Machine Learning Methods for Inversion of Integral Transformations and Their Application to Experimental Data Analysis (Ref. 2021-018)
Dimiter Petsev, Ph.D., Professor, Department of Chemical & Biological Engineering
Researchers at the University of New Mexico and Los Alamos National Laboratory have developed a new machine learning based hybrid inverse method, to overcome limitations of the existing methods. The previously disclosed method, hNMF, is based on unsupervised machine learning. The proposed method has been demonstrated on a set of normal distributions; however, it can also be applied to other types of distributions in a similar manner. In addition, the method is applicable to integrals with different kernels and can be applied to arbitrarily distributed systems, eliminating limitations associated with focus on normal particle size distributions. Learn about the technology here.
Ion Exchange Membrane Separated Two Electrode Flow Analyzer for Continuous Aqueous Electrochemical Heavy Metal Detection (Ref. 2020-107)
Fernando Garzon, Ph.D., Distinguished Professor, Department of Chemical & Biological Engineering, and Director, Center for Micro-Engineering Materials
Researchers at the University of New Mexico have developed an inline electrochemical cell, to analyze heavy metal concentrations in water. Comprised of an ion exchange membrane (IEM) and corresponding electrodes, this novel invention allows for control of ion selectivity and diffusivity; as well as, determining the pH of the analysis. Increased sensitivity, selectivity and ease of use enable this technology to sample and analyze a wide range of potential metal analytes. In addition, by allowing continuous water source determination and creating a stable potential reference electrode, this electrochemical cell can substantially impact aqueous heavy metal analysis. Learn about the technology here.
Technique for Multi-Stream Electron Beam Generation for High Power Microwave Sources (Ref. 2021-076)
Edl Schamiloglu, Ph.D., Associate Dean for Research and Innovation, UNM School of Engineering and Distinguished Professor, Department of Electrical and Computer Engineering
Researchers at the University of New Mexico (UNM) aim to broaden the present knowledge of multiple electron beam high-power amplifiers, by developing a model of a device capable of generating two electron beams with at least > 10% energy difference and comparable currents. As such, they have developed a method and apparatus related to generating multi-stream electron beams with different energies and comparable currents from a single cathode stalk at a single potential using nested magnetically insulated coaxial diodes (MICDs). The analytical and numerical simulations, based on the SINUS-6 pulsed power electron beam accelerator at UNM, exhibit comparable results. A case study approach was followed to identify an optimal geometry from this nested MICD model. An optimal geometry is a crucial factor in achieving the maximum energy difference between the electron beams for comparable currents. As a result, the analytical and simulation work presented here provide new insights into high power dual-beam technology. Learn about the technology here.
Low Loss Tunable Matching Network for Pattern Reconfigurable Array Antennas (Ref. 2020-103)
Marios Patriotis, Ph.D., Research Assistant, Department of Electrical & Computer Engineering
Researchers at the University of New Mexico (UNM) have developed a low loss reconfigurable array antenna. The reconfigurable tuning network is used to create an independent radio frequency (RF) switch that maintains stable tuning and highly efficient system. The active elements can be any RF switch type, are able to activate and deactivate RF energy, and can be used to form single RF-port array antennas. Moreover, switches can be implemented to establish a beamforming antenna, eliminating the complexity of transceivers used to control each individual RF. The reconfigurable switching system provides independent multiple beam steering, at any desired frequency, while embracing low-cost and straightforward simplicity characteristics. Moreover, this can be used to approach different reconfigurable antenna models; such as, polarization diverse or a combination of other radiating characteristics. Learn about the technology here.