Vassili Karanassios
Biography
Vassili Karanassios' interests are in the area of micro- and nano-analysis (e.g., metrology), in micro- and nano-technology (e.g., micro- and nano-fluidics, nano-materials), and in development of miniaturized instruments that can be used on-site (i.e., in the field). Such instruments are typically fabricated on-chips so that they can fit in the palm of a hand or in a shirt pocket, thus allowing users to take "the lab to the sample". The sample may be a "patient" (for early diagnosis of disease) and the field may be a health clinic or the environment (e.g., the air we breathe or the water we drink). In addition, such instruments are being developed to have wireless-capabilities so that they can be included in the Internet of Things (IoT) and to have some smarts (via Artificial Intelligence, primarily using Artificial Neural Networks and Deep Learning approaches).
Research Interests
Plasma and microplasma spectrochemistry for chemical analysis
Miniaturized, portable, low flow-rate battery-operated microplasmas
Miniaturization of Chemical analysis instrumentation
Elemental Analysis of micro- and nano-samples
Applications of Nanoscience and Nanotechnology in Analytical Chemistry
Microfluidics and nanofluidics
Miniaturization of sensors and of Analytical instruments
Nanomaterials and their analytical chemistry applications
Quantum Dots for analytical applications
Artificial Intelligence (and its variants, e.g., machine learning) and their applications to analytical chemistry
Teaching*
- CHEM 123 - General Chemistry 2
- Taught in 2026
- CHEM 221 - Multi-Component Analysis
- Taught in 2021, 2022, 2025
- CHEM 323 - Analytical Instrumentation
- Taught in 2021, 2022, 2023, 2024, 2025
- CHEM 400 - Special Topics in Chemistry
- Taught in 2021, 2022, 2023, 2024, 2025
- CHEM 794 - Master's Seminar
- Taught in 2025, 2026
* Only courses taught in the past 5 years are displayed.
Selected/Recent Publications
T. Farup and V. Karanassios, “Artificial intelligence and related topics (e.g. machine learning, artificial neural networks, deep learning, reinforcement learning) as they used to address UN environmental challenges”, Next-Generation Spectroscopic Technologies XVII; SPIE Proc., Volume 13449, 1344905 (2025); https://doi.org/10.1117/12.3053865
S. Runqi Wu, V. Karanassios, “Portable tribo-electric nano-generators (TENGS) for miniaturized sensors”, SPIE Proc., Next-Generation Spectroscopic Technologies XVII; Volume 13449, 134490H (2025); https://doi.org/10.1117/12.3053875
V. Karanassios, L. Quadeer, C. Zhang, “Recent developments in battery-operated microplasmas for chemical analysis”, SPIE ProcNext-Generation Spectroscopic Technologies XVII; Volume 13449, 134490B (2025); https://doi.org/10.1117/12.3053896
D. Cebula, U. Dayal, M. J. W. Thiessen, C. Dismore and V. Karanassios, “Graphene, a two-dimensional (2D) nano-material, for chemical speciation in the field”; Next-Generation Spectroscopic Technologies XVI, SPIE Proc., 13026, 130260E (2024); https://doi.org/10.1117/12.3013598
C. Tat and V. Karanassios, “Artificial intelligence and related topics, (e.g., machine learning, deep learning, Artificial Neural Networks or ANNs) as applied to the teaching and to the practice of analytical spectrochemistry”; Next-Generation Spectroscopic Technologies XVI, SPIE Proc., Volume 13026, 130260H (2024); https://doi.org/10.1117/12.3013600
V. Karanassios, U. Dayal, M. J. W. Thiessen, “Quantum dots for selected environmental analysis applications”, Next-Generation Spectroscopic Technologies XVI, SPIE Proc., Volume 13026, 1302609 (2024); https://doi.org/10.1117/12.3013593
Introduction to Artificial Intelligence (AI)-including Artificial Neural Networks (ANN), Deep Learning (DL), Machine Learning (ML) and some of their applications to Plasma and to Microplasma Spectrochemistry, a 4-hour short course offered at the Winter Conference on Plasma Spectrochemistry, Tucson, Arizona, January 4, 2024
Artificial Neural Networks (ANN), Deep Learning (DL), Machine Learning (ML)), Celine Tat and Vassili Karanassios, Next generation Spectroscopic Technologies XV, Proc. SPIE 12516, 1251602 (15 June 2023); DOI: 10.1117/12.2664911
Artificial intelligence, machine learning, and deep learning in plasma and microplasma spectrochemistry, Celine Tat and Vassili Karanassios, Next generation Spectroscopic Technologies XV, Proc. SPIE 12516, 1251602 (15 June 2023); DOI: 10.1117/12.2664911 (we have been working on AI and its various named-versions for a long time, the first AI-thesis from my group was in 2001).
Further development in triboelectric energy harvesting for microsensors or nanosensors and microintruments for use on-site, S. R. Wu; N. Lim; J. A. Millar; and V. Karanassios, Next-Generation Spectroscopic Technologies XV, Proc. SPIE 12516, 125160M (15 June 2023); DOI: 10.1117/12.2664912
Continued developments on microplasmas for analytical spectroscopy, V. Karanassios, L. Quadeer, U. Dayal, and M. J. W. Thiessen, Next-Generation Spectroscopic Technologies XV, Proc. SPIE 12516, 1251608 (15 June 2023); DOI: 10.1117/12.2664909
Impression of a symposium on novel spectrochemical instrumentation and microplasmas, V. Karanassios, ICP Inf. News Letter, 47(8), 791-792 (2022).
Progress and characterization of battery-operated microplasmas by a portable optical emission spectrometer, Laiba Quadeer, Vera Zhou and Vassili Karanassios, Applied Optical Metrology IV, SPIE Proc 11817, 118170L (2021); DOI: 10.1117/12.2598545
Chromium speciation using graphene oxide as a chemo-mechanical material and a battery operated microplasma on a polymeric substrate, Daniel A. Cebula and Vassili Karanassios, 2021 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), June 2021, DOI:10.1109/FLEPS51544.2021.9469751