Bi2Te3-Based Thermoelectric Modules for Efficient and Reliable Low-Grade Heat Recovery.

Bismuth-telluride-based alloy has long been considered as the most promising candidate for low-grade waste heat power generation. However, optimizing the thermoelectric performance of n-type Bi2Te3 is more challenging than that of p-type counterparts due to its greater sensitivity to texture, and thus limits the advancement of thermoelectric modules. Herein, the thermoelectric performance of n-type Bi2Te3 is enhanced by incorporating a small amount of CuGaTe2, resulting in a peak ZT of 1.25 and a distinguished average ZT of 1.02 (300-500 K). The decomposed Cu+ strengthens interlayer interaction, while Ga+ optimizes carrier concentration within an appropriate range. Simultaneously, the emerged numerous defects, such as small-angle grain boundaries, twin boundaries, and dislocations, significantly suppresses the lattice thermal conductivity. Based on the size optimization by finite element modelling, the constructed thermoelectric module yields a high conversion efficiency of 6.9% and output power density of 0.31 W cm-2 under a temperature gradient of 200 K. Even more crucially, the efficiency and output power little loss after subjecting the module to 40 thermal cycles lasting for 6 days. This study demonstrates the efficient and reliable Bi2Te3-based thermoelectric modules for broad applications in low-grade heat harvest.

Fully inkjet-printed Ag2Se flexible thermoelectric devices for sustainable power generation.

Flexible thermoelectric devices show great promise as sustainable power units for the exponentially increasing self-powered wearable electronics and ultra-widely distributed wireless sensor networks. While exciting proof-of-concept demonstrations have been reported, their large-scale implementation is impeded by unsatisfactory device performance and costly device fabrication techniques. Here, we develop Ag2Se-based thermoelectric films and flexible devices via inkjet printing. Large-area patterned arrays with microscale resolution are obtained in a dimensionally controlled manner by manipulating ink formulations and tuning printing parameters. Printed Ag2Se-based films exhibit (00 l)-textured feature, and an exceptional power factor (1097 µWm-1K-2 at 377 K) is obtained by engineering the film composition and microstructure. Benefiting from high-resolution device integration, fully inkjet-printed Ag2Se-based flexible devices achieve a record-high normalized power (2 µWK-2cm-2) and superior flexibility. Diverse application scenarios are offered by inkjet-printed devices, such as continuous power generation by harvesting thermal energy from the environment or human bodies. Our strategy demonstrates the potential to revolutionize the design and manufacture of multi-scale and complex flexible thermoelectric devices while reducing costs, enabling them to be integrated into emerging electronic systems as sustainable power sources.

High-efficiency and reliable same-parent thermoelectric modules using Mg3Sb2-based compounds.

Thermoelectric modules can convert waste heat directly into useful electricity, providing a clean and sustainable way to use fossil energy more efficiently. Mg3Sb2-based alloys have recently attracted considerable interest from the thermoelectric community due to their nontoxic nature, abundance of constituent elements and excellent mechanical and thermoelectric properties. However, robust modules based on Mg3Sb2 have progressed less rapidly. Here, we develop multiple-pair thermoelectric modules consisting of both n-type and p-type Mg3Sb2-based alloys. Thermoelectric legs based on the same parent fit into each other in terms of thermomechanical properties, facilitating module fabrication and ensuring low thermal stress. By adopting a suitable diffusion barrier layer and developing a new joining technique, an integrated all-Mg3Sb2-based module demonstrates a high efficiency of 7.5% at a temperature difference of 380 K, exceeding the state-of-the-art same-parent thermoelectric modules. Moreover, the efficiency remains stable during 150 thermal cycling shocks (∼225 h), demonstrating excellent module reliability.

Single cell and spatial alternative splicing analysis with long read sequencing.

Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to explore alternative splicing at single cell and spatial resolution. The higher sequencing error of long reads, especially high indel rates, have limited the accuracy of cell barcode and unique molecular identifier (UMI) recovery. Read truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, can cause the false detection of spurious new isoforms. Downstream, there is yet no rigorous statistical framework to quantify splicing variation within and between cells/spots. In light of these challenges, we developed Longcell, a statistical framework and computational pipeline for accurate isoform quantification for single cell and spatial spot barcoded long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the two data modalities. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.

Giotto: a toolbox for integrative analysis and visualization of spatial expression data.

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.