A5083684552- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Advanced Battery Technologies Research
- Crystallization and Solubility Studies
- Control Systems and Identification
- Advancements in Battery Materials
- Viral Infectious Diseases and Gene Expression in Insects
- Probabilistic and Robust Engineering Design
- Process Optimization and Integration
- Stability and Control of Uncertain Systems
- Advanced Battery Materials and Technologies
- Analytical Chemistry and Chromatography
- Innovative Microfluidic and Catalytic Techniques Innovation
- Spectroscopy and Chemometric Analyses
- Protein purification and stability
- Mineral Processing and Grinding
- Silicon and Solar Cell Technologies
- Electrodeposition and Electroless Coatings
- Neural Networks and Applications
- Reliability and Maintenance Optimization
- Machine Learning in Materials Science
- nanoparticles nucleation surface interactions
- Freezing and Crystallization Processes
- Advanced Control Systems Design
- Chemical and Physical Properties in Aqueous Solutions
Massachusetts Institute of Technology
2016-2025
Boston University
2014-2025
Cell Biotech (South Korea)
2023
Biocat
2023
University of Cambridge
2020-2021
Jet Propulsion Laboratory
2020-2021
University of Strathclyde
2020-2021
Xidian University
2020-2021
Air Products (United States)
2020-2021
University of Illinois Urbana-Champaign
2008-2020
The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations existing technology include underutilization, stress-induced material damage, capacity fade, the potential thermal runaway. This paper reviews efforts in modeling simulation batteries their use design better batteries. Likely future directions including promising research opportunities are outlined.
A series of tubes: The continuous manufacture a finished drug product starting from chemical intermediates is reported. pilot-scale plant used novel route that incorporated many advantages continuous-flow processes to produce active pharmaceutical ingredients and the in one integrated system. As service our authors readers, this journal provides supporting information supplied by authors. Such materials are peer reviewed may be re-organized for online delivery, but not copy-edited or...
Consumer electronics, wearable and personal health devices, power networks, microgrids, hybrid electric vehicles (HEVs) are some of the many applications lithium-ion batteries. Their optimal design management important for safe profitable operations. The use accurate mathematical models can help in achieving best performance. This article provides a detailed description finite volume method (FVM) pseudo-two-dimensional (P2D) Li-ion battery model suitable development model-based advanced...
Infections associated with orthopedic implants cause increased morbidity and significant healthcare cost. A prolonged expensive two-stage procedure requiring two surgical steps a 6–8 week period of joint immobilization exists as today's gold standard for the revision arthroplasty an infected prosthesis. Because infection is much more common in implant replacement surgeries, these issues greatly impact long-term patient care continually growing part population. Here, we demonstrate that...
Stochastic uncertainties are ubiquitous in complex dynamical systems and can lead to undesired variability of system outputs and, therefore, a notable degradation closed-loop performance. This paper investigates model predictive control nonlinear subject probabilistic parametric uncertainties. A framework is presented for the probability distribution states while ensuring satisfaction constraints with some desired levels. To obtain computationally tractable formulation real applications,...
Forecasting the health of a battery is modeling effort that critical to driving improvements in and adoption electric vehicles. Purely physics-based models purely data-driven have advantages limitations their own. Considering nature data end-user applications, we outline several architectures for integrating machine learning can improve our ability forecast lifetime. We discuss ease implementation, advantages, limitations, viability each architecture, given state art fields.