Tuning Piezoelectricity via Thermal Annealing at a Freestanding Ferroelectric Membrane.

Tuning the ferroelectric domain structure by a combination of elastic and electrostatic engineering provides an effective route for enhanced piezoelectricity. However, for epitaxial thin films, the clamping effect imposed by the substrate does not allow aftergrowth tuning and also limits the electromechanical response. In contrast, freestanding membranes, which are free of substrate constraints, enable the tuning of a subtle balance between elastic and electrostatic energies, giving new platforms for enhanced and tunable functionalities. Here, highly tunable piezoelectricity is demonstrated in freestanding PbTiO3 membranes, by varying the ferroelectric domain structures from c-dominated to c/a and a domains via aftergrowth thermal treatment. Significantly, the piezoelectric coefficient of the c/a domain structure is enhanced by a factor of 2.5 compared with typical c domain PbTiO3. This work presents a new strategy to manipulate the piezoelectricity in ferroelectric membranes, highlighting their great potential for nano actuators, transducers, sensors and other NEMS device applications.

Nanoceria as an Electron Reservoir: Spontaneous Deposition of Metal Nanoparticles on Oxides and Their Anti-inflammatory Activities.

Designing metal-metal oxide heteronanostructures with synergistic and superior activities (unattainable in the case of a single entity) is of great interest for a wide range of technological applications. Traditional synthetic strategies typically require reducing agents, stabilizing ligands, or high temperature reductive treatment to produce oxide-supported metals. Herein, a facile noble metal deposition strategy is developed to produce silver, gold, and platinum nanocrystals on the surface of hollow mesoporous cerium oxide nanospheres without any pretreatment. Unlike the galvanic replacement reaction, the developed protocol employs the innate reductive potential of CeO2 to produce a high density of ultrafine noble metal nanocrystals homogeneously immobilized onto the surface of CeO2 nanospheres. The multienzyme-like activities (i.e., superoxide dismutase-like and catalase-like) of CeO2@metal nanostructures, originating from CeO2 and metal nanoparticles, were effectively utilized for anti-inflammatory therapies in two in vivo models. This oxygen vacancy-mediated reduction strategy can be generalized to produce diverse metal-metal oxide nanostructures for a wide range of applications.

Single-Atom Tailoring of Two-Dimensional Atomic Crystals Enables Highly Efficient Detection and Pattern Recognition of Chemical Vapors.

Low-dimensional semiconductor materials, such as single-walled carbon nanotubes, two-dimensional (2D) atomic crystals, and organic frameworks, have been widely adapted as ideal platforms to construct various chemo/biosensors with satisfying sensitivity. However, the general drawbacks in chemiresistive devices, including high operation temperatures, low response to low-polarity molecules, and poor selectivity, have limited their real-world applications. In this study, 2D materials (graphene, MoS2, and WSe2) were systematically functionalized with series of monodispersed single atomic sites (Pt, Co, and Ru) through a facile approach to construct single-atom sensors (SASs) for the detection of VOCs at room temperature. The structural and catalytic characteristics of SAs successfully translated into enhanced gas-sensing performance, with a 1-2 orders of magnitude increase in relative response to ethanol (@5 ppm) and acetone (@20 ppm) vapors (in all M-2D SASs as compared to pristine substrates), high selectivity to VOCs against relative humidity (M-WSe2 SASs), and fast response/recovery time (11/58 s for Pt-Graphene and 22/48 s for Pt-MoS2 to 50 ppm ethanol, 9/57 s for Pt-Graphene and 15/75 s for Pt-MoS2 to 200 ppm acetone) that are several times faster than the pristine 2D materials. Density functional theory (DFT) calculations revealed the signaling mechanism in SASs, and the data were further trained to build machine learning (ML) models for predicting the adsorption energies and sensing performance using the features of adsorption heights, metal charge, and charge transfer between the adsorbed VOCs and SASs sites. Finally, the rich combination of the metal single atoms and 2D atomic crystal supports were converted to cross-sensitive SA sensor array that allows for detection and identification of different VOCs.