Thermoelectric composites are known for their enhanced power conversion performance via interfacial engineering and intensified mechanical, structural, and thermal properties. However, the selection of these nanoinclusions, for example, their type, size effect, volume fraction, distribution uniformity, coherency with host, carrier dynamics, and physical stability, plays a crucial role in modifying the host material thermoelectric properties. In this Review, we classify the nanoinclusions into five types: carbon allotropes, secondary thermoelectric phases, metallic materials, insulating oxides, and others. On the basis of the classification, we discuss the mechanisms involved in improving the ZT of nanocomposites involving reduction of thermal conductivity (κ) by phonon scattering, improving the Seebeck coefficient (α) via energy filtering effect and the electrical conductivity (σ) by carrier injection or carrier channeling. Comprehensibly, we validate that adding nanoinclusions with high electrical and low thermal conductivity as compared to the matrix material is the best way to optimize the interlocked thermoelectric parameters. Thus, collective doping and nanoinclusions in thermoelectric materials is the best possible solution to achieve a higher power conversion efficiency equivalent to other renewable energy technologies.

In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the characteristics of synaptic functions in the human brain. In this aspect, this study designs and develops CsFAPbI3‐based memristive neuromorphic devices that can switch at low power and show larger endurance by adopting the powder engineering methodology. The neuromorphic characteristics of the CsFAPbI3‐based devices exhibit an ultra‐high paired‐pulse facilitation index for an applied electric stimuli pulse. Moreover, the transition from short‐term to long‐term memory requires ultra‐low energy with long relaxation times. The learning and training cycles illustrate that the CsFAPbI3‐based devices exhibit faster learning and memorization process owing to their larger carrier lifetime compared to other perovskites. The results provide a pathway to attain low‐power neuromorphic devices that are synchronic to the human brain's performance.

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids.

Recently, copper-based chalcogenides, especially sulfides, have attracted considerable attention due to their inexpensive, earth-abundance, nontoxicity, and good thermoelectric performance. Cu 3 SbS 4 is one such kind with p-type conductivity and high phase stability for potential medium-temperature applications. In this article, the effect of a multiwalled carbon nanotube (MWCNT) on the thermoelectric parameters of Cu 3 SbS 4 is studied. A facile synthesis route of mechanical alloying (MA), followed by hot pressing (HP) was utilized to achieve dense and fine-grain samples. Adding the optimal amount of MWCNT nanoinclusions in Cu 3 SbS 4 enhanced the Seebeck coefficient by carrier energy filtering and reduced the thermal conductivity by strong phonon scattering mechanisms. This synergistic optimization helped achieve the maximum figure of merit (ZT) of 0.43 in the 3 mol % MWCNT nanoinclusion composite sample, which is 70% higher than the pristine Cu 3 SbS 4 at 623 K. In addition, enhancement in mechanical stability is observed with the increasing nanoinclusion concentration. Dispersion strengthening and grain boundary hardening mechanisms help improve mechanical stability in the nanocomposite samples. Apart from the enhanced mechanical stability, our study highlights that the incorporation of multiwalled CNT nanoinclusions boosted the thermoelectric performance of Cu 3 SbS 4 , and the same strategy can be extended to other next-generation and conventional thermoelectric materials.

No abstract