Multi-heterojunctioned plastics with high thermoelectric figure of merit.

Conjugated polymers promise inherently flexible and low-cost thermoelectrics for powering the Internet of Things from waste heat1,2. Their valuable applications, however, have been hitherto hindered by the low dimensionless figure of merit (ZT)3-6. Here we report high-ZT thermoelectric plastics, which were achieved by creating a polymeric multi-heterojunction with periodic dual-heterojunction features, where each period is composed of two polymers with a sub-ten-nanometre layered heterojunction structure and an interpenetrating bulk-heterojunction interface. This geometry produces significantly enhanced interfacial phonon-like scattering while maintaining efficient charge transport. We observed a significant suppression of thermal conductivity by over 60 per cent and an enhanced power factor when compared with individual polymers, resulting in a ZT of up to 1.28 at 368 kelvin. This polymeric thermoelectric performance surpasses that of commercial thermoelectric materials and existing flexible thermoelectric candidates. Importantly, we demonstrated the compatibility of the polymeric multi-heterojunction structure with solution coating techniques for satisfying the demand for large-area plastic thermoelectrics, which paves the way for polymeric multi-heterojunctions towards cost-effective wearable thermoelectric technologies.

A drug-mediated organic electrochemical transistor for robustly reusable biosensors.

Reusable point-of-care biosensors offer a cost-effective solution for serial biomarker monitoring, addressing the critical demand for tumour treatments and recurrence diagnosis. However, their realization has been limited by the contradictory requirements of robust reusability and high sensing capability to multiple interactions among transducer surface, sensing probes and target analytes. Here we propose a drug-mediated organic electrochemical transistor as a robust, reusable epidermal growth factor receptor sensor with striking sensitivity and selectivity. By electrostatically adsorbing protonated gefitinib onto poly(3,4-ethylenedioxythiophene):polystyrene sulfonate and leveraging its strong binding to the epidermal growth factor receptor target, the device operates with a unique refresh-in-sensing mechanism. It not only yields an ultralow limit-of-detection concentration down to 5.74 fg ml-1 for epidermal growth factor receptor but, more importantly, also produces an unprecedented regeneration cycle exceeding 200. We further validate the potential of our devices for easy-to-use biomedical applications by creating an 8 × 12 diagnostic drug-mediated organic electrochemical transistor array with excellent uniformity to clinical blood samples.

Social intelligence mediates the protective role of resting-state brain activity in the social cognition network against social anxiety.

Background: Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety. Objective: This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety. Methods: Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety. Results: Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus. Further, the left SFG fALFF was positively correlated with social anxiety; brain-personality-symptom analysis revealed that this relationship was mediated by social intelligence. Conclusion: These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity → higher social intelligence → lower social anxiety. These may have implication for developing neurobehavioral interventions to mitigate social anxiety.

Nanoscale doping of polymeric semiconductors with confined electrochemical ion implantation.

Nanoresolved doping of polymeric semiconductors can overcome scaling limitations to create highly integrated flexible electronics, but remains a fundamental challenge due to isotropic diffusion of the dopants. Here we report a general methodology for achieving nanoscale ion-implantation-like electrochemical doping of polymeric semiconductors. This approach involves confining counterion electromigration within a glassy electrolyte composed of room-temperature ionic liquids and high-glass-transition-temperature insulating polymers. By precisely adjusting the electrolyte glass transition temperature (Tg) and the operating temperature (T), we create a highly localized electric field distribution and achieve anisotropic ion migration that is nearly vertical to the nanotip electrodes. The confined doping produces an excellent resolution of 56 nm with a lateral-extended doping length down to as little as 9.3 nm. We reveal a universal exponential dependence of the doping resolution on the temperature difference (Tg - T) that can be used to depict the doping resolution for almost infinite polymeric semiconductors. Moreover, we demonstrate its implications in a range of polymer electronic devices, including a 200% performance-enhanced organic transistor and a lateral p-n diode with seamless junction widths of <100 nm. Combined with a further demonstration in the scalability of the nanoscale doping, this concept may open up new opportunities for polymer-based nanoelectronics.

Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.