Aside from band gap reduction, little is understood about the effect of the tin‐for‐lead substitution on the fundamental optical and optoelectronic properties of metal halide perovskites (MHPs), especially when transitioning from 3D to lower dimensional structures. Herein, we take advantage of the spectroscopic isolation of excitons in 2D MHPs to study the intrinsic differences between lead and tin MHPs. The exciton's spectral fine structure indicates a larger polaron binding energy in tin MHPs. Additionally, the electroabsorption responses of the 2D MHPs demonstrates that tin MHPs have exciton binding energies 1.5–2× lower than that of their lead counterparts. Despite the lower binding energy, the excitons in tin MHPs are more Frenkel‐like with small radii, small polarizabilities, and large dipole moments. These results are interpreted as consequences of small polaron formation and disorder‐induced dipole moments. This work highlights the wide range of intrinsic differences between lead and tin MHPs as well as the complexity of excited states in these systems.

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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 were collected over two temperature ranges: 10–300 K and 298–319 K. The accuracy of each neural network was assessed via a 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.

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