Souhail Essadiki,  Alain Carvalho,  Fabrice Vigneron,  Guillaume Fleith,  Jérôme Combet,  Doru Constantin,   Nicolas Stein,  and  Laure Biniek

This study describes a reproducible process for forming highly mesoporous, mechanically robust, and handleable aerogels based on entangled poly(3-hexylthiophene) (P3HT) and syndiotactic polystyrene (sPS) nanofibers for thermoelectric applications. The highly porous structure results in low thermal conductivity, allowing the temperature difference (between the hot and the cold side) to be maintained across the aerogel sample. Porosity also enables dopants to diffuse efficiently within the sample. When using 2,3,5,6-tetrafluoro-7,7,8,8-tetracyano-quinodimethane (F₄TCNQ), the highest dopant uptake leads to a maximum apparent electrical conductivity of 2 × 10⁻² S cm⁻¹ and a Seebeck coefficient (58 µV K⁻¹) close to the values obtained in thin films. The Seebeck coefficient is not affected by the high porosity of the material. To improve the doping level of the P3HT:sPS aerogels, FeCl₃ or a mixture of F₄TCNQ:FeCl₃ is also used as a dopant. This enhances the power factor (0.2 µW m⁻¹K⁻²) without significantly increasing the thermal conductivity (30–40 mW m⁻¹ K⁻¹). Finally, the use of the doped aerogel as a vertical thermoelectric generator with one leg is demonstrated by generating a few tens of nW at a thermal difference of 11 K. This result highlights the potential for integrating these polymer aerogels into wearable thermoelectric generators for powering microelectronics.

Khoukha Khoussa, Patrick Lévêque, Larbi Boubchir

Organic Photovoltaic (OPV) Devices Have Emerged as a Promising Alternative to Conventional Solar Cells due to Their Flexibility, Lightweight Nature, and Potential for Low-cost Production. However, Optimizing OPV Performance Remains a Complex Challenge, Traditionally Requiring Extensive Experimental Trials or Computational Chemistry Approaches Based on Molecular Descriptors. To Accelerate the Development of High-efficiency OPVs, Artificial Intelligence (AI) Has Been Increasingly Utilized, Particularly Machine Learning Models That Rely on Chemical Descriptors. While these Methods Have Shown Success, They Are Often Limited by the Quality and Completeness of the Selected Descriptors, Potentially Overlooking Key Structural and Morphological Information. In this Work, We Propose a Novel Deep Learning Framework Leveraging Convolutional Neural Networks (CNNs) to Predict OPV Performance Directly from 2D Images of Donor and Acceptor Materials. By Employing a Customized Representation of Molecular Structures, Our Approach Captures Spatial and Hierarchical Patterns That Traditional Descriptors Based ML Models May Miss. We Compare Our Model's Predictive Capability to Conventional Machine Learning Techniques and Demonstrate Its Potential for Improving Prediction Accuracy and Generalization without Need to Add the Frontier Molecular Orbitals (FMOs) to Enhance Predictions. Our Findings Highlight the Power of Deep Learning in Accelerating the Discovery of Efficient Organic Photovoltaic Materials, Paving the Way for a Data-driven Approach to Materials Science and Device Optimization