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 (FeCl 3 −) for a bis(trifluoromethanesulfonamide) (TFSI −) anion in the polymers poly(3-hexylthiophene-2,5-diyl) (P3HT) and poly[3-(2,2-bithien-5yl)-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, FeCl 3 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, FeCl 3 is a weak dopant, but the exchange of FeCl 3 − for TFSI − is spontaneous, so the doping level increases by >10× with AED.

In this study, we use a modeling approach to evaluate the potential impact of microbial metabolism on the organic composition of cloud droplets and atmospheric aerosols. Microbial consumption rates for small organic molecules typically found in cloud and aerosol water were incorporated into a 0-D multiphase photochemical atmospheric chemistry model. We then use the model to simulate the evolution of the organic content of individual cloud and aerosol particles, along with the atmospheric gas phase. We find that metabolically active microorganisms may significantly impact organic acid concentrations in the individual aerosols and cloud droplets in which they reside. However, as a result of the low density of metabolically active cells in the atmosphere, the impact of these processes on the chemical composition of the overall population of cloud droplets or aerosols, or on gas-phase chemistry, is likely negligible.

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.

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Elevated expression of the complement component 4A (C4A) protein has been linked to an increased risk of schizophrenia (SCZ). However, there are few human models available to study the mechanisms by which C4A contributes to the development of SCZ. In this study, we established a C4A overexpressing neuroimmune cortical organoid (NICO) model, which includes mature neuronal cells, astrocytes, and functional microglia. The C4A NICO model recapitulated several neuroimmune endophenotypes observed in SCZ patients, including modulation of inflammatory genes and increased cytokine secretion. C4A expression also increased microglia-mediated synaptic uptake in the NICO model, supporting the hypothesis that synapse and brain volume loss in SCZ patients may be due to excessive microglial pruning. Our results highlight the role of C4A in the immunogenetic risk factors for SCZ and provide a human model for phenotypic discovery and validation of immunomodulating therapies.