Jeehyun Jeong

Organic thin-film thermoelectric generators (TEGs) hold great promise as flexible and wearable energy harvesters. However, their broader application is hindered by the difficulty of establishing a temperature gradient within the thin film when using planar heat sources. Here, we introduce pseudo-transverse TEGs (pT-TEGs), an architecture employing an elastic substrate with two thermal conductivities, enabling the orthogonal conversion of longitudinal heat flux into lateral thermovoltage by mimicking the transverse thermoelectric effect. This design circumvents the limitations of conventional thin-film TEGs, such as the need for three-dimensional deformation to form a temperature gradient, allowing efficient thermal energy harvesting from planar heat sources while preserving a two-dimensional form factor. Furthermore, the fully solution-processed and modular pT-TEGs exhibit excellent mechanical flexibility and high scalability. We believe that this approach offers a practical platform for the realization of genuine two-dimensional TEGs and will contribute to the advancement of flexible and wearable energy harvesting technologies.

Colloidal quantum dot (QD) films inherently exhibit a high Seebeck coefficient ( α ) and low thermal conductivity ( κ ), making them attractive for thermoelectric (TE) applications. However, achieving high electrical conductivity ( σ ) while preserving these advantages remains challenging due to limited interdot coupling and high‐temperature treatments that compromise interdependent TE parameters. Herein, room‐temperature inter‐dot bridging of individual PbS QDs is demonstrated, leading to unprecedented electronic tunability and significant improvements in the n‐type TE figure of merit ( ZT ). By tailoring the hydride concentration, gradual removal of surface ligands is induced, resulting in strengthened electrical coupling, structural ordering, and n‐type doping of QD films. Consequently, σ is precisely modulated from ≈10 ⁻⁶ to ≈10 ² S cm ⁻¹ , beyond the degeneracy transition regime where the maximum power factor (PF) is achieved. Importantly, a low κ of ≈0.25 W m ⁻¹ K ⁻¹ is preserved, elucidating the preferential charge transport enhancement without compromising phonon scattering. These effects collectively yield a substantially increased PF (103 µW m ⁻¹ K ⁻² ) and ZT = 0.14 at 330 K, which are among the highest reported for n‐type TE thin‐films processed under ambient conditions. This strategy provides a scalable, universal pathway for optimizing TE transport properties in QD‐based devices, offering strong potential for flexible thermoelectric applications.

Optimizing the processing conditions of conjugated polymers is a key step in improving the performance of various organic electronic devices, including organic thermoelectric (TE) devices that are capable of harvesting energy from waste heat. However, the inherent structural and energetic disorders in these materials and their unpredictable property changes after processing complicate the optimization principles, requiring numerous trial‐and‐error experiments to maximize the TE performance. Here, a machine‐learning (ML)‐based design of experiments approach is introduced to address these challenges, and its effectiveness is demonstrated using a representative thiophene‐based doped polymer system for organic TE. This approach not only helps to quantitatively understand how each processing parameter affects the TE properties of the polymer but also facilitates the prediction of optimal processing conditions, leading to achieve maximum TE performance with minimal experiments within a large parameter space. Furthermore, the ML‐based approach is validated by revealing the origins of the power factor maximization at the ML‐predicted processing conditions through analysis of the morphology and electronic states of the doped polymer films. The proposed methodology is applicable to the majority of polymer systems for organic thermoelectric energy conversion and will provide guidelines for determining optimal process conditions and improving TE performance with minimal effort.