Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics.

Many biological systems have a narrow temperature range of operation, meaning high accuracy and spatial distribution level are needed to study these systems. Most temperature sensors cannot meet both the accuracy and spatial distribution required in the microfluidic systems that are often used to study these systems in isolation. This paper introduces a neural network called the Multi-Directional Fluorescent Temperature Long Short-Term Memory Network (MFTLSTM) that can accurately calculate the temperature at every pixel in a fluorescent image to improve upon the standard fitting practice and other machine learning methods use to relate fluorescent data to temperature.

Effect of Workpiece/Tool Heat Transfer and Friction Coefficients on Accuracy of Simulated Temperatures and Torques in a Friction Stir Welding Plunge.

Friction stir process models are typically validated by tuning heat transfer and friction coefficients until measured temperatures in either the tool or workpiece, but rarely in both, match simulated results. A three-dimensional finite element model for a tool plunge in an AA 6061-T6 is validated for temperature predictions in both the tool and workpiece using a friction coefficient that varies with time. Peak workpiece temperatures were within 1.

Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics.

Many biological systems have a narrow temperature range of operation, meaning high accuracy and spatial distribution level are needed to study these systems. Most temperature sensors cannot meet both the accuracy and spatial distribution required in the microfluidic systems that are often used to study these systems in isolation. This paper introduces a neural network called the Multi-Directional Fluorescent Temperature Long Short-Term Memory Network (MFTLSTM) that can accurately calculate the temperature at every pixel in a fluorescent image to improve upon the standard fitting practice and other machine learning methods use to relate fluorescent data to temperature.

3D-Printed Microfluidic One-Way Valves and Pumps.

New microfluidic lab-on-a-chip capabilities are enabled by broadening the toolkit of devices that can be created using microfabrication processes. For example, complex geometries made possible by 3D printing can be used to approach microfluidic design and application in new or enhanced ways. In this paper, we demonstrate three distinct designs for microfluidic one-way (check) valves that can be fabricated using digital light processing stereolithography (DLP-SLA) with a poly(ethylene glycol) diacrylate (PEGDA) resin, each with an internal volume of 5-10 nL.

Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data Toward Use in Bio-microfluidics.

Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near ± K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature.

3D printing-enabled uniform temperature distributions in microfluidic devices.

Many microfluidic processes rely heavily on precise temperature control. Though internally-contained heaters have been developed using traditional fabrication methods, they are limited in their ability to isothermally heat a precisely defined volume. Advances in 3D printing have led to high resolution printers capable of using bio-compatible materials and achieving geometry resolutions near 20 μm.

Use of Machine Learning with Temporal Photoluminescence Signals from CdTe Quantum Dots for Temperature Measurement in Microfluidic Devices.

Because of the vital role of temperature in many biological processes studied in microfluidic devices, there is a need to develop improved temperature sensors and data analysis algorithms. The photoluminescence (PL) of nanocrystals (quantum dots) has been successfully used in microfluidic temperature devices, but the accuracy of the reconstructed temperature has been limited to about 1 K over a temperature range of tens of degrees. A machine learning algorithm consisting of a fully-connected network of seven layers with decreasing numbers of nodes was developed and applied to a combination of normalized spectral and time-resolved PL data of CdTe quantum dot emission in a microfluidic device.

Fluorescent scanning thermal microscope based on a Blu-ray optical head to measure thermal diffusivity of radioactive samples.

Over the life of nuclear fuel, inhomogeneous structures develop, negatively impacting thermal properties. New fuels are under development but require more accurate knowledge of how the properties change to model performance and determine safe operational conditions. Measurement systems capable of microscopic thermal transport measurements and low cost are necessary to measure these properties and integrate into hot cells where electronics are likely to fail during fuel investigation.

Investigation of synthetic spider silk crystallinity and alignment via electrothermal, pyroelectric, literature XRD, and tensile techniques.

The processes used to create synthetic spider silk greatly affect the properties of the produced fibers. This paper investigates the effect of process variations during artificial spinning on the thermal and mechanical properties of the produced silk. Property values are also compared to the ones of the natural dragline silk of the spider, and to unprocessed (as-spun) synthetic silk.