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. The data used by the algorithm was collected over two temperature ranges: 10 K to 300 K, and 298 K to 319 K. The accuracy of each neural network was assessed via mean absolute error of a holdout set of data. For the low temperature regime, the accuracy was 7.7 K, or 0.4 K when the holdout set is restricted to temperatures above 100 K. For the high temperature regime, the accuracy was 0.1 K. This method provides demonstrates a potential machine learning approach to accurately sense temperature in microfluidic (and potentially nanofluidic) devices when the data analysis is based on normalized PL data when it is stable over time.

Permanganate-based synthesis of manganese oxide nanoparticles in ferritin.

This paper investigates the comproportionation reaction of MnII with [Formula: see text] as a route for manganese oxide nanoparticle synthesis in the protein ferritin. We report that [Formula: see text] serves as the electron acceptor and reacts with MnII in the presence of apoferritin to form manganese oxide cores inside the protein shell. Manganese loading into ferritin was studied under acidic, neutral, and basic conditions and the ratios of MnII and permanganate were varied at each pH. The manganese-containing ferritin samples were characterized by transmission electron microscopy, UV/Vis absorption, and by measuring the band gap energies for each sample. Manganese cores were deposited inside ferritin under both the acidic and basic conditions. All resulting manganese ferritin samples were found to be indirect band gap materials with band gap energies ranging from 1.01 to 1.34 eV. An increased UV/Vis absorption around 370 nm was observed for samples formed under acidic conditions, suggestive of MnO2 formation inside ferritin.

Tuning Ferritin's band gap through mixed metal oxide nanoparticle formation.

This study uses the formation of a mixed metal oxide inside ferritin to tune the band gap energy of the ferritin mineral. The mixed metal oxide is composed of both Co and Mn, and is formed by reacting aqueous Co2+ with [Formula: see text] in the presence of apoferritin. Altering the ratio between the two reactants allowed for controlled tuning of the band gap energies. All minerals formed were indirect band gap materials, with indirect band gap energies ranging from 0.52 to 1.30 eV. The direct transitions were also measured, with energy values ranging from 2.71 to 3.11 eV. Tuning the band gap energies of these samples changes the wavelengths absorbed by each mineral, increasing ferritin's potential in solar-energy harvesting. Additionally, the success of using [Formula: see text] in ferritin mineral formation opens the possibility for new mixed metal oxide cores inside ferritin.