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The objective of this paper is to evaluate the capability of an Artificial Neural Network to classify the thermal conductivity of water-glycol mixture in various concentrations. Massive training/validation/test temperature data were created by using a COMSOL model for geometry including a micropipette thermal sensor in an infinite media (i.e., water-glycol mixture) where a 500 ?s laser pulse is irradiated at the tip. The randomly generated temporal profile of the temperature dataset was then fed into a trained ANN to classify the thermal conductivity of the mixtures, whose value would be used to distinguish the glycol concentration at a sensitivity of 0.2% concentration with an accuracy of 96.5%. Training of the ANN yielded an overall classification accuracy of 99.99% after 108 epochs. 

To understand and quantify the thermal energy transfer in a biological cell, the measurement of thermal properties at a cellular level is emerging as great importance. We report herein a unique technique that utilizes a laser point heat source for temporal temperature rise in a micro-pipette thermal sensor; this technique characterizes heat conduction of a measured sample, the Jurkat cell, thus measuring the sample's thermal conductivity (TC). To this end, we incorporated the computational model in COMSOL to solve for the transient temperature and used the multi-parameter fitting of the experimental data using MATLAB. To address the influence of a Jurkat cell's chemical composition on TC, we compared three structural models for prediction of effective thermal conductivity in heterogeneous materials thereby determining the weight percentage of the Jurkat cell. When considering water and protein as the major constituents, we found that a combination of Maxwell-Euken and Effective Medium Theory modeling provides the closest approximation to published weight percent data and, therefore, is recommended for prediction of the cell composition. We validate the accuracy of the measurement technique, itself, by measuring polyethylene microspheres and observed 1% deviation from published data. The unique technique was determined to be mechanically non-invasive, capable of maintaining viable cells, and capable of measuring the thermal conductivity of a Jurkat cell, which was demonstrated to be 0.538 W/(m⋅K) ± 1%.