Advanced Electronic Materials最新文献

Thermoelectric (TE) charge transport in organic TE nanocomposite systems is a critical consideration in designing high-performance TE materials. Here, the relationship between the TE properties and energy structure of conducting polymer/quantum dot (QD) nanocomposites is systematically investigated by developing a potential wall or potential well in poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) with CdTe QDs. The added QDs are primarily distributed within the electrically insulating PSS shell and act as stepping stones for charge transport between PEDOT-rich grains. The embedded QDs generate an energy-filtering effect, which is induced by both potential wall and potential well states established by the QDs in the PEDOT:PSS films. The induced energy-filtering effect increases the Seebeck coefficient S with limited loss of electrical conductivity σ, thereby overcoming the TE trade-off relation S ∝ σ ^−1/4. The energy-filtering effect is optimized by carefully controlling the QD size. The PEDOT:PSS/QD nanocomposite containing the smallest QDs exhibits a power factor of 173.8 µW m⁻¹ K⁻², which is 80% larger than the value for the pristine PEDOT:PSS film. This work suggests a strategy for designing TE nanocomposites with improved TE performance and emphasizes the importance of fine-tuning the interfacial energy gap to achieve an effective energy-filtering effect.

The lateral carrier profile of amorphous indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) plays a significant role in determining the effective channel length (L_eff) and length scalability even when the physical gate length (L_g) is the same. Especially, devices with high carrier concentration that have a high mobility of 14.54 cm² V·s⁻¹ suffer from severe short channel effects at L_g = 1 µm due to the reduced L_eff. The current work proposes a systematic methodology for optimizing length scalability for a given L_g that involves engineering of the lateral carrier profile. Unique lateral carrier profiles are extracted using contour maps of ΔL and R_SD as a function of carrier profile parameters, and they are validated by comparing the measured L_eff, drain-to-source resistance, and current-voltage characteristics with the results of simulations using the extracted carrier profiles. Further, to overcome the trade-off between enhanced mobility and degraded V_T roll-off that occurs with increasing carrier concentration, an IGZO TFT with gate-insulator shoulders is fabricated to structurally form negative ΔL and physically increase L_eff, while also obtaining a high carrier concentration, ultimately achieving both optimal electrical performance, with mobility of 17.50 cm² V·s⁻¹, and complete control of the electrostatic integrity of the gate.

Self-attention mechanism is critically central to the state-of-the-art transformer models. Because the standard full self-attention has quadratic complexity with respect to the input's length L, resulting in prohibitively large memory for very long sequences, sparse self-attention enabled by random projection (RP)-based locality-sensitive hashing (LSH) has recently been proposed to reduce the complexity to O(L log L). However, in current digital computing hardware with a von Neumann architecture, RP, which is essentially a matrix multiplication operation, incurs unavoidable time and energy-consuming data shuttling between off-chip memory and processing units. In addition, it is known that digital computers simply cannot generate provably random numbers. With the emerging analog memristive technology, it is shown that it is feasible to harness the intrinsic device-to-device variability in the memristor crossbar array for implementing the RP matrix and perform RP-LSH computation in memory. On this basis, sequence prediction tasks are performed with a sparse self-attention-based Transformer in a hybrid software-hardware approach, achieving a testing accuracy over 70% with much less computational complexity. By further harnessing the cycle-to-cycle variability for multi-round hashing, 12% increase in the testing accuracy is demonstrated. This work extends the range of applications of memristor crossbar arrays to the state-of-the-art large language models (LLMs).

Exploiting simulation modeling of graphene synaptic field-effect transistors is extremely important for helping researchers to construct carbon-based neuromorphic computing systems. Here, lateral-ionic-gated graphene synaptic FETs with different gate lengths are fabricated, and they are modeled by using basic physic models combined with the ions migration-diffusion model and graphene material model. The feasibility and accuracy of the proposed modeling are validated by showing an excellent agreement between simulations and experimental results. The slicing technique of the modeling is proposed to analyze the influence of ionic concentration and diffusion coefficient on the ions movement to reveal their working mechanism. The effect of key parameters about gate length, ionic concentration, and diffusion coefficient on synaptic behavior such as short-term plasticity, and long-term plasticity is simulated and discussed. In addition, three kinds of spike-timing-dependent plasticity are obtained by the device modeling. This research opens up promising avenues for the development of artificial synapse modeling and paths to new opportunities for the construction of carbon-based neuromorphic networks.

Conductive fillers-embedded plastic polymer hybrid composites are crucial electronic materials in modern technologies owing to their tunable conductive properties, low density, and corrosion resistance. However, the application of traditional rigid inorganic conductive fillers-embedded plastic composites is constrained by their subpar electrical conductivity and mechanical properties. Consequently, liquid metals (LMs) including gallium and gallium-based alloys, have recently emerged as the preferred conductive flexible fillers over traditional rigid fillers. This work employs a solvent evaporation method and phase separation-induced conductivity mechanism. The resulting flexible and electrically conductive LM-polycarbonate (PC) film circuits demonstrate ultrahigh metallic conductivity, robust mechanical performance, excellent solvent recyclability, and notable processability. The fluidic nature of LMs and the superior mechanical properties of the PC polymer confer high electrical stability and durability to the LM-PC film circuits under diverse mechanical forces and environmental conditions. The LM-PC film circuits are exceedingly promising and apt for use as flexible conductors in contemporary electrical applications, including electricity transmission and underwater working.

Revealing transient electronic properties in anisotropic 2D materials is a prerequisite for developing ultrafast optoelectronic functional devices. Here, the ultrafast electronic dynamics of polarization anisotropy for the AB-stacked rhenium disulfide (ReS₂) are studied using time- and energy-resolved photoemission electron microscopy. The ultrafast electronic relaxation process exhibits sensitive layer-dependent polarization anisotropy. The linear dichroism of the ultrafast electronic dynamics is also measured, indicating that the polarization anisotropy is determined by the fast process on the sub-picosecond scale. With ab initio theory calculations, it is confirmed that the ultra-sensitive layer dependence of the lifetime of the fast process originates from a stronger interlayer coupling in the K-Γ direction (along the b-axis) of ReS₂. Further, by analyzing the time-resolved photoemission energy spectrum, distinct “fast” and “slow” regimes are found in the ultrafast dynamics of excited electrons with different energies. The corresponding energy windows also show substantial polarization anisotropy, which is associated with the linear dichroism of the electron-phonon coupling in the AB-stacked ReS₂. This work has implications for the design of angle-sensitive optoelectronic functional devices with the AB-stacked ReS₂.

The synthesis of a strontium hexaferrite magnet is studied using in situ synchrotron powder X-ray diffraction (PXRD) with a 16-ms time resolution. The precursor material is cold compacted shape-controlled goethite and strontium carbonate. The time evolution of the phases is modeled with sequential Rietveld refinements revealing that strontium hexaferrite forms within seconds at ≈1173 K. Texture analysis is performed on selected PXRD frames throughout the experiment, and the preferred orientation introduced by cold-pressing goethite prevails through the iron oxide phase transitions (goethite → hematite → strontium hexaferrite). Electron backscatter diffraction (EBSD) data on the final pellet confirms the preferred orientation observed with PXRD. The resulting magnet has respectable magnetic properties, considering the simplicity of the preparation method, with an energy product (BH_max) of 18.6(8) kJ m⁻³.

Elemental phosphorus in its various allotropes has received tremendous research attention recently due to its intriguing electronic and structural properties. Notably, the application of nanostructured materials to overcome the inherent flaws in bulk materials is promising. However, many challenges need to be addressed before its widespread implementation. Thus, a specific tenet to design novel and robust nanomaterials is a decisive factor in the desired outcome, and the most daunting task before realizing this is solving the Schrödinger equation. First principle density functional theory (DFT) calculations have emerged as an insightful and accurate design tool to investigate the structural, electronic, and possible synthesis scenarios of yet undiscovered materials at atomic levels. In this review, the basic principles and the importance of DFT are discussed, followed by a summary of recent advances in the first principle study of elemental phosphorus-based nanomaterials. Elemental phosphorus-based nanomaterials and their allotropes have attracted growing interest in the renewable energy community due to their modulable product selectivity. However, the understanding of the physical phenomena of allotropic modification is still lacking. Therefore, the aim is to motivate experimental researchers to conduct DFT studies and experiments to comprehend relevant engineered nanomaterials better. Finally, the challenges and potential future research directions for further theoretical and computational development of phosphorus-based nanomaterials are outlined.

Optoelectronic Artificial Synapses

Lu Wang and co-workers have fabricated a bioartificial synapse composited with egg albumen and carbon nanotubes (see article number 2300631). The electrical characteristics of the contact interface between carbon nanotubes doped with Fe substitution and Al electrode are analyzed by first principles, and the adsorption, charge distribution, and band structure between them are studied.

Sustainable Soft Electronics

This cover, referring to article number 2300792 by Orlin D. Velev, Yong Zhu, and co-workers, illustrates the concept of sustainable soft electronics with biodegradable substrate (agarose/glycerol) and recyclable functional material (silver nanowires). The background represents the molecular structure of the biodegradable substrate with interaction between the agarose polymer helices (blue) and glycerol molecules (red and white). The soft electronic circuit is flexible and stretchable.