Flexible and degradable pressure sensors have received tremendous attention for potential use in transient electronic skins, flexible displays, and intelligent robotics due to their portability, real-time sensing performance, flexibility, and decreased electronic waste and environmental impact. However, it remains a critical challenge to simultaneously achieve a high sensitivity, broad sensing range (up to 30 kPa), fast response, long-term durability, and robust environmental degradability to achieve fullscale biomonitoring and decreased electronic waste. MXenes, which are two-dimensional layered structures with a large specific surface area and high conductivity, are widely employed in electrochemical energy devices. Here, we present a highly sensitive, flexible, and degradable pressure sensor fabricated by sandwiching porous MXene-impregnated tissue paper between a biodegradable polylactic acid (PLA) thin sheet and an interdigitated electrode-coated PLA thin sheet. The flexible pressure sensor exhibits high sensitivity with a low detection limit (10.2 Pa), broad range (up to 30 kPa), fast response (11 ms), low power consumption (10 −8 W), great reproducibility over 10 000 cycles, and excellent degradability. It can also be used to predict the potential health status of patients and act as an electronic skin (E-skin) for mapping tactile stimuli, suggesting potential in personal healthcare monitoring, clinical diagnosis, and nextgeneration artificial skins.
The development of integrated high-performance supercapacitors with all-in-one configuration, excellent flexibility and autonomously intrinsic self-healability, and without the extra healable film layers, is still tremendously challenging. Compared to the sandwich-like laminated structures of supercapacitors with augmented interfacial contact resistance, the flexible healable integrated supercapacitor with all-in-one structure could theoretically improve their interfacial contact resistance and energy densities, simplify the tedious device assembly process, prolong the lifetime, and avoid the displacement and delamination of multilayered configurations under deformations. Herein, a flexible healable all-in-one configured supercapacitor with excellent flexibility and reliable self-healing ability by avoiding the extra healable film substrates and the postassembled sandwich-like laminated structures is developed. The healable all-in-one configured supercapacitor is prepared from in situ polymerization and deposition of nanocomposites electrode materials onto the two-sided faces of the self-healing hydrogel electrolyte separator. The self-healing hydrogel film is obtained from the physically crosslinked hydrogel with enormous hydrogen bonds, which can endow the healable capability through dynamic hydrogen bonding. The assembled all-in-one configured supercapacitor exhibits enhanced capacitive performance, good cycling stability, reliable self-healing capability, and excellent flexibility. It holds broad prospects for obtaining various flexible healable all-in-one configured supercapacitors for working as portable energy storage devices in wearable electronics.
Flexible wearable pressure sensors have drawn tremendous interest for various applications in wearable healthcare monitoring, disease diagnostics, and human–machine interaction. However, the limited sensing range (<10%), low sensing sensitivity at small strains, limited mechanical stability at high strains, and complicated fabrication process restrict the extensive applications of these sensors for ultrasensitive full‐range healthcare monitoring. Herein, a flexible wearable pressure sensor is presented with a hierarchically microstructured framework combining microcrack and interlocking, bioinspired by the crack‐shaped mechanosensory systems of spiders and the wing‐locking sensing systems of beetles. The sensor exhibits wide full‐range healthcare monitoring under strain deformations of 0.2–80%, fast response/recovery time (22 ms/20 ms), high sensitivity, the ultrasensitive loading sensing of a feather (25 mg), the potential to predict the health of patients with early‐stage Parkinson's disease with the imitated static tremor, and excellent reproducibility over 10 000 cycles. Meanwhile, the sensor can be assembled as smart artificial electronic skins (E‐skins) for simultaneously mapping the pressure distribution and shape of touching sensing. Furthermore, it can be attached onto the legs of a smart robot and coupled to a wireless transmitter for wirelessly monitoring human‐motion interactivities.
Pyridinium has been shown to be a cocatalyst for the electrochemical reduction of CO 2 on metal and semiconductor electrodes, but its exact role has been difficult to elucidate. In this work, we create cooperative cobaltprotoporphyrin (CoPP) and pyridine/pyridinium (py/pyH + ) catalytic sites on metal−organic layers (MOLs) for an electrocatalytic CO 2 reduction reaction (CO 2 RR). Constructed from [Hf 6 (μ 3 -O) 4 (μ 3 -OH) 4 (HCO 2 ) 6 ] secondary building units (SBUs) and terpyridine-based tricarboxylate ligands, the MOL was postsynthetically functionalized with CoPP via carboxylate exchange with formate capping groups. The CoPP group and the pyridinium (pyH + ) moiety on the MOL coactivate CO 2 by forming the [pyH + -− O 2 C-CoPP] adduct, which enhances the CO 2 RR and suppresses hydrogen evolution to afford a high CO/ H 2 selectivity of 11.8. Cooperative stabilization of the [pyH + -− O 2 C-CoPP] intermediate led to a catalytic current density of 1314 mA/mgCo for CO production at −0.86 V RHE , which corresponds to a turnover frequency of 0.4 s −1 .
CO2 hydrogenation to ethanol is of practical
importance
but poses a significant challenge due to the need of forming one C–C
bond while keeping one C–O bond intact. CuI centers
could selectively catalyze CO2-to-ethanol conversion, but
the CuI catalytic sites were unstable under reaction conditions.
Here we report the use of low-intensity light to generate CuI species in the cavities of a metal–organic framework (MOF)
for catalytic CO2 hydrogenation to ethanol. X-ray photoelectron
and transient absorption spectroscopies indicate the generation of
CuI species via single-electron transfer from photoexcited
[Ru(bpy)3]2+-based ligands on the MOF to CuII centers in the cavities and from Cu0 centers
to the photoexcited [Ru(bpy)3]2+-based ligands.
Upon light activation, this Cu–Ru–MOF hybrid selectively
hydrogenates CO2 to EtOH with an activity of 9650 μmol
gCu
–1 h–1 under 2 MPa
of H2/CO2 = 3:1 at 150 °C. Low-intensity
light thus generates and stabilizes CuI species for sustained
EtOH production.
Discovery and optimization of new
catalysts can be potentially
accelerated by efficient data analysis using machine-learning (ML).
In this paper, we record the process of searching for additives in
the electrochemical deposition of Cu catalysts for CO2 reduction
(CO2RR) using ML, which includes three iterative cycles:
“experimental test; ML analysis; prediction and redesign”.
Cu catalysts are known for CO2RR to obtain a range of products
including C1 (CO, HCOOH, CH4, CH3OH) and C2+ (C2H4, C2H6, C2H5OH, C3H7OH). Subtle changes in morphology and surface structure of the catalysts
caused by additives in catalyst preparation can lead to dramatic shifts
in CO2RR selectivity. After several ML cycles, we obtained
catalysts selective for CO, HCOOH, and C2+ products. This
catalyst discovery process highlights the potential of ML to accelerate
material development by efficiently extracting information from a
limited number of experimental data.
Fe3O4 is one of the promising anode materials in Li-ion batteries and a potential alternative to graphite due to the high specific capacity, natural abundance, environmental benignity, non-flammability, and better...
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