Desirée L Plata
Biographic Data
| ID | 7994317 |
|---|---|
| NAME | Desirée L Plata |
| GIVEN NAMES | Desirée L |
| FAMILY NAME | Plata |
| SIGNATURE | PLATA D L |
| AFFILIATIONS | Massachusetts Institute of Technology |
| ORCID | 0000-0003-0657-7735 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Assessment of groundwater well vulnerability to contamination through physics-informed machine learning
Contamination from anthropogenic activities is a long-standing challenge to the sustainability of groundwater resources. Physically based (PB) models are often used in groundwater risk assessments, but their application to large scale problems requiring high spatial resolution remains computationally intractable. Machine learning (ML) models have emerged as an alternative to PB models in the era of big data, but the necessary number of observatio…
Life Cycle Assessment of Aerogel Manufacture on Small and Large Scales
Recent studies demonstrated that advanced aerogel composites (Aspen Aerogels ® Spaceloft ® [SL]) have the potential to transform oil remediation via high oil uptake capacity and selectivity, excellent reusability, and high mechanical strength. Understanding the life cycle environmental impacts of advanced aerogels can enable a more holistic decision‐making process when considering oil recovery technologies following a spill. Here, we perform a cr…
No prominent works on this page.
Life Cycle Assessment of Aerogel Manufacture on Small and Large Scales
Recent studies demonstrated that advanced aerogel composites (Aspen Aerogels ® Spaceloft ® [SL]) have the potential to transform oil remediation via high oil uptake capacity and selectivity, excellent reusability, and high mechanical strength. Understanding the life cycle environmental impacts of advanced aerogels can enable a more holistic decision‐making process when considering oil recovery technologies following a spill. Here, we perform a cr…
Assessment of groundwater well vulnerability to contamination through physics-informed machine learning
Contamination from anthropogenic activities is a long-standing challenge to the sustainability of groundwater resources. Physically based (PB) models are often used in groundwater risk assessments, but their application to large scale problems requiring high spatial resolution remains computationally intractable. Machine learning (ML) models have emerged as an alternative to PB models in the era of big data, but the necessary number of observatio…
Contamination (2 works) · Environmental Science (2 works) · Aerogel (1 work) · Aerogels and thermal insulation (1 work) · Aquifer (1 work) · Atmospheric and Environmental Gas Dynamics (1 work) · Building materials and conservation (1 work) · Chemistry (1 work) · Computer Science (1 work) · Engineering (1 work)