Fabrication of ultra-fine nanostructures using edge transfer printing

Touching the theoretical capacity: synthesizing cubic LiTi2(PO4)3/C nanocomposites for high-performance lithium-ion battery

A cubic LiTi2(PO4)3/C composite is successfully prepared via a simple solvothermal method and further glucose-pyrolysis treatment. The as-fabricated LTP/C material delivers an ultra-high reversible capacity of 144 mA h g-1 at 0.2C rate, which is the highest ever reported, and shows considerable performance improvement compared with before. Combining this with the stable cycling performance and high rate capability, such material has a promising future in practical application.

Auto-generated iron chalcogenide microcapsules ensure high-rate and high-capacity sodium-ion storage

Sodium-ion batteries (SIBs) are regarded as promising alternative energy-storage devices to lithium-ion batteries (LIBs). However, the trade-off of between energy density and power density under high mass-loading conditions restricts the application of SIBs. Herein, we synthesized an FeSe@FeS material via a facile solid-state reaction. A microcapsule architecture was spontaneously achieved in this process, which facilitated electron transport and provided stable diffusion paths for Na ions. The FeSe@FeS material exhibits a high capacity retention (485 mA h g^-1 at 3 A g^-1 after 1400 cycles) and superior rate capability (230 mA h g^-1 at 10 A g^-1 after 1600 cycles) in the half-cell test. Furthermore, superior cycling stability is achieved in the full-cell test. The high mass-loaded FeSe@FeS electrodes (8 mg cm^-2) realize a high areal capacity retention of 2.8 mA h cm^-2 and high thermal stability.

Unprecedented sensitivity towards pressure enabled by graphene foam

Reduced graphene oxide foam (RGOF)-based pressure sensors have been fabricated through the combination of ultrasonic dispersion and freeze-drying methods. Due to the maintenance of the highly disordered structure of the ultrasonic dispersed graphene oxides before the freezing process, the RGOF sensors demonstrated an ultra-high sensitivity of 22.8 kPa^-1, an ultra-low detection limit of around 0.1 Pa, and a superior separation of 0.2-Pascal-scale difference.

Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning

Plasmonic sensors have been used for a wide range of biological and chemical sensing applications. Emerging nanofabrication techniques have enabled these sensors to be cost-effectively mass manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nanosensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multispectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nanosensors, we select the optimal set of illumination light-emitting diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nanohole arrays and revealed that the optimal illumination bands differ from those that are "intuitively" selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multispectral readers, helping the translation of nanosensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.

Single-crystal-conjugated polymers with extremely high electron sensitivity through template-assisted in situ polymerization

Single-crystal-conjugated polymer (SCCP) arrays are prepared successfully via a simple method, which is a combination of the contact thermochemical reaction and solvent-free in situ polymerization. The dramatic X-ray diffraction and selective-area electron diffraction results show the high crystallinity of the SCCP arrays. These SCCP arrays display unique physical properties and show great potential in flexible electronics.

Controllable fabrication of oriented micro/nanowire arrays of dibenzo-tetrathiafulvalene by a multiple drop-casting method

A multiple drop-casting method of growing the ultralong dibenzo-tetrathiafulvalene (DB-TTF) micro/nanowire arrays has been developed which has the success ratio as high as 94%. This method enables the arrays with a length over a few hundreds of micrometers to locate between droplets with the definite orientation. The width of the micro/nanowires is controlled via tuning the concentration of DB-TTF solution in dichloromethane. The large-scale arrays can be grown onto Si, SiO₂, glass, and the flexible polyethylene terephthalate (PET) substrates. These results show the promising potential of this facile solution-based process for the growth of the high-quality organic micro/nanowires, the fabrication of high-performance and flexible devices, and the fabrication of controlled assemblies of nanoscale circuits for fundamental studies and future applications.