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Abstract Chemical doping is an important approach to manipulating charge-carrier concentration and transport in organic semiconductors (OSCs)^1–3and ultimately enhances device performance^4–7. However, conventional doping strategies often rely on the use of highly reactive (strong) dopants^8–10, which are consumed during the doping process. Achieving efficient doping with weak and/or widely accessible dopants under mild conditions remains a considerable challenge. Here, we report a previously undescribed concept for the photocatalytic doping of OSCs that uses air as a weak oxidant (p-dopant) and operates at room temperature. This is a general approach that can be applied to various OSCs and photocatalysts, yielding electrical conductivities that exceed 3,000 S cm^–1. We also demonstrate the successful photocatalytic reduction (n-doping) and simultaneous p-doping and n-doping of OSCs in which the organic salt used to maintain charge neutrality is the only chemical consumed. Our photocatalytic doping method offers great potential for advancing OSC doping and developing next-generation organic electronic devices. 

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications. 

With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity on prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it. In this paper, we study the accuracy disparity problem in regression. To begin with, we first propose an error decomposition theorem, which decomposes the accuracy disparity into the distance between marginal label distributions and the distance between conditional representations, to help explain why such accuracy disparity appears in practice. Motivated by this error decomposition and the general idea of distribution alignment with statistical distances, we then propose an algorithm to reduce this disparity, and analyze its game-theoretic optima of the proposed objective functions. To corroborate our theoretical findings, we also conduct experiments on five benchmark datasets. The experimental results suggest that our proposed algorithms can effectively mitigate accuracy disparity while maintaining the predictive power of the regression models. 

Abstract Conducting polymers, such as thep-doped poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), have enabled the development of an array of opto- and bio-electronics devices. However, to make these technologies truly pervasive, stable and easily processable,n-doped conducting polymers are also needed. Despite major efforts, non-type equivalents to the benchmark PEDOT:PSS exist to date. Here, we report on the development of poly(benzimidazobenzophenanthroline):poly(ethyleneimine) (BBL:PEI) as an ethanol-basedn-type conductive ink. BBL:PEI thin films yield ann-type electrical conductivity reaching 8 S cm⁻¹, along with excellent thermal, ambient, and solvent stability. This printablen-type mixed ion-electron conductor has several technological implications for realizing high-performance organic electronic devices, as demonstrated for organic thermoelectric generators with record high power output andn-type organic electrochemical transistors with a unique depletion mode of operation. BBL:PEI inks hold promise for the development of next-generation bioelectronics and wearable devices, in particular targeting novel functionality, efficiency, and power performance. 

Abstract A new approach to control the n‐doping reaction of organic semiconductors is reported using surface‐functionalized gold nanoparticles (f‐AuNPs) with alkylthiols acting as the catalyst only upon mild thermal activation. To demonstrate the versatility of this methodology, the reaction of the n‐type dopant precursor N‐DMBI‐H with several molecular and polymeric semiconductors at different temperatures with/without f‐AuNPs, vis‐à‐vis the unfunctionalized catalyst AuNPs, was investigated by spectroscopic, morphological, charge transport, and kinetic measurements as well as, computationally, the thermodynamic of catalyst activation. The combined experimental and theoretical data demonstrate that while f‐AuNPs is inactive at room temperature both in solution and in the solid state, catalyst activation occurs rapidly at mild temperatures (~70 °C) and the doping reaction completes in few seconds affording large electrical conductivities (~10–140 S cm⁻¹). The implementation of this methodology enables the use of semiconductor+dopant+catalyst solutions and will broaden the use of the corresponding n‐doped films in opto‐electronic devices such as thin‐film transistors, electrochemical transistors, solar cells, and thermoelectrics well as guide the design of new catalysts.