• Energy Research
• 2021-2025close
• 7. Clean energy close
• Neuroscience close

The actuation of micro- and nanostructures controlled by external stimuli remains one of the exciting challenges in nanotechnology due to the wealth of fundamental questions and potential applications in energy harvesting, robotics, sensing, biomedicine, and tunable metamaterials. Photoactuation utilizes the conversion of light into motion through reversible chemical and physical processes and enables remote and spatiotemporal control of the actuation. Here, we report a fast light-to-motion conversion in few-nanometer thick bare polydopamine (PDA) membranes stimulated by visible light. Light-induced heating of PDA leads to desorption of water molecules and contraction of membranes in less than 140 μs. Switching off the light leads to a spontaneous expansion in less than 20 ms due to heat dissipation and water adsorption. Our findings demonstrate that pristine PDA membranes are multiresponsive materials that can be harnessed as robust building blocks for soft, micro-, and nanoscale actuators stimulated by light, temperature, and moisture level.

Animals digest food to fuel brain neurometabolism via cellular respiration. This study demonstrates the combination of a biofuel cell (BFC) and an animal brain stimulator (ABS) implanted in a pigeon. Glucose oxidation and oxygen reduction in an enzymatic BFC supplied electrical power to the ABS. Power from the BFC reached 0.12 mW in vitro and 0.08 mW in vivo using only the natural glucose and oxygen in the pigeon's body. A power management integrated circuit is used to harvest energy from the in vivo BFC at a rate of 28.4 mJ over 10 min, which is sufficient for intermittent neurostimulation.

A little-studied p-type ternary oxide semiconductor, copper(I) tungstate (Cu2WO4), was assessed by a combined theoretical/experimental approach. A detailed computational study was performed to solve the long-standing debate on the space group of Cu2WO4, which was determined to be triclinic P1. Cu2WO4 was synthesized by a time-efficient, arc-melting method, and the crystalline reddish particulate product showed broad-band absorption in the UV-visible spectral region, thermal stability up to ∼260 °C, and cathodic photoelectrochemical activity. Controlled thermal oxidation of copper from the Cu(I) to Cu(II) oxidation state showed that the crystal lattice could accommodate Cu2+ cations up to ∼260 °C, beyond which the compound was converted to CuO and CuWO4. This process was monitored by powder X-ray diffraction and X-ray photoelectron spectroscopy. The electronic band structure of Cu2WO4 was contrasted with that of the Cu(II) counterpart, CuWO4 using spin-polarized density functional theory (DFT). Finally, the compound Cu2WO4 was determined to have a high-lying (negative potential) conduction band edge underlining its promise for driving energetic photoredox reactions.

Achieving sophisticated juxtaposition of geared molecular rotors with negligible energy-requirements in solids enables fast yet controllable and correlated rotary motion to construct switches and motors. Our endeavor was to realize multiple rotors operating in a MOF architecture capable of supporting fast motional regimes, even at extremely cold temperatures. Two distinct ligands, 4,4'-bipyridine (bipy) and bicyclo[1.1.1]pentanedicarboxylate (BCP), coordinated to Zn clusters fabricated a pillar-and-layer 3D array of orthogonal rotors. Variable temperature XRD, 2H solid-echo, and 1H T1 relaxation NMR, collected down to a temperature of 2 K revealed the hyperfast mobility of BCP and an unprecedented cascade mechanism modulated by distinct energy barriers starting from values as low as 100 J mol-1 (24 cal mol-1), a real benchmark for complex arrays of rotors. These rotors explored multiple configurations of conrotary and disrotary relationships, switched on and off by thermal energy, a scenario supported by DFT modeling. Furthermore, the collective bipy-ring rotation was concerted with the framework, which underwent controllable swinging between two arrangements in a dynamical structure. A second way to manipulate rotors by external stimuli was the use of CO2, which diffused through the open pores, dramatically changing the global rotation mechanism. Collectively, the intriguing gymnastics of multiple rotors, devised cooperatively and integrated into the same framework, gave the opportunity to engineer hypermobile rotors (107 Hz at 4 K) in machine-like double ligand MOF crystals.

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.

In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.