Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency.

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

Anion Exchange Doping: Tuning Equilibrium to Increase Doping Efficiency in Semiconducting Polymers.

High electron affinity (EA) molecules p-type dope low ionization energy (IE) polymers, resulting in an equilibrium doping level based on the energetic driving force (IE-EA), reorganization energy, and dopant concentration. Anion exchange doping (AED) is a process whereby the dopant anion is exchanged with a stable ion from an electrolyte. We show that the AED level can be predicted using an isotherm equilibrium model. The exchange of the dopant anion (FeCl3-) for a bis(trifluoromethanesulfonamide) (TFSI-) anion in the polymers poly(3-hexylthiophene-2,5-diyl) (P3HT) and poly[3-(2,2-bithien-5-yl)-2,5-bis(2-hexyldecyl)-2,5-dihydropyrrolo[3,4-c]pyrrole-1,4-dione-6,5-diyl] (PDPP-2T) highlights two cases in which the process is nonspontaneous and spontaneous, respectively. For P3HT, FeCl3 provides a high doping level but an unstable counterion, so exchange results in an air stable counterion with a marginal increase in doping. For PDPP-2T, FeCl3 is a weak dopant, but the exchange of FeCl3- for TFSI- is spontaneous, so the doping level increases by >10× with AED.