Approximately 2.8 billion people rely on polluting fuels (e.g. wood, kerosene) for cooking. With affordability being a key access barrier to clean cooking fuels, such as liquefied petroleum gas (LPG), pay-as-you-go (PAYG) LPG smart meter technology may help resource-poor households adopt LPG by allowing incremental fuel payments. To understand the potential for PAYG LPG to facilitate clean cooking, objective evaluations of customers’ cooking and spending patterns are needed. This study uses novel smart meter data collected between January 2018-June 2020, spanning COVID-19 lockdown, from 426 PAYG LPG customers living in an informal settlement in Nairobi, Kenya to evaluate stove usage (e.g. cooking events/day, cooking event length). Seven semi-structured interviews were conducted in August 2020 to provide context for potential changes in cooking behaviours during lockdown. Using stove monitoring data, objective comparisons of cooking patterns are made with households using purchased 6 kg cylinder LPG in peri-urban Eldoret, Kenya. In Nairobi, 95% of study households continued using PAYG LPG during COVID-19 lockdown, with consumption increasing from 0.97 to 1.22 kg/capita/month. Daily cooking event frequency also increased by 60% (1.07 to 1.72 events/day). In contrast, average days/month using LPG declined by 75% during lockdown (17 to four days) among seven households purchasing 6 kg cylinder LPG in Eldoret. Interviewed customers reported benefits of PAYG LPG beyond fuel affordability, including safety, time savings and cylinder delivery. In the first study assessing PAYG LPG cooking patterns, LPG use was sustained despite a COVID-19 lockdown, illustrating how PAYG smart meter technology may help foster clean cooking access.

With the presence of a driving field applied to double quantum dots and a control field applied on the cavity, the transmission performance and group delay effect of a probe field have been theoretically studied in a hybrid optomechanical system (HOMS). Due to the interaction between the mechanical mode and the double quantum dots system, double optomechanically induced transparency (OMIT) arises in the HOMS. With the assistance of a driving field, the system can be tuned to switch on any one of the two OMIT windows, switch on both of the two OMIT windows or switch off both of the two OMIT windows by dynamically adjusting control of the optical field and the driving field. Furthermore, the transmitted probe fields of the two OMIT windows can be tuned to be absorbed or amplified with proper parameters of the driving field and control field. Moreover, the transmission properties of the two OMIT windows are asymmetrical. One can obtain the maximum group delay time of the probe field by optimizing the amplitude and phase of the driving field. These results provide a new way for constructing optically controlled nanostructured photonic switch and storage devices.

Heterometallic Anderson wheels of formula [(VIVO)2MII5(hmp)10Cl2](ClO4)2·2MeOH (M = Ni, 1; Co, 2) have been synthesised from the solvothermal reaction of M(ClO4)2·6H2O and VCl3 with hmpH (2-(hydroxymethyl)pyridine). The metallic skeleton describes a centred hexagon, with the two vanadyl ions sitting on opposing sides of the outer ring. Magnetic susceptibility and magnetisation measurements indicate the presence of both ferromagnetic and antiferromagnetic exchange interactions. Theoretical calculations based on density functional methods reproduce both the sign and strength of the exchange interactions found experimentally, and rationalise the parameters extracted.

Herein, we report on the sub-picosecond to sub-nanosecond vibrational energy transfer (VET) dynamics of the solid energetic material 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) using broadband, ultrafast infrared transient absorption spectroscopy. Experiments reveal VET occurring on three distinct timescales: sub-picosecond, 5 ps, and 200 ps. The ultrafast appearance of signal at all probed modes in the mid-infrared suggests strong anharmonic coupling of all vibrations in the solid whereas the long-lived evolution demonstrates that VET is incomplete, and thus thermal equilibrium is not attained, even on the hundred picosecond timescale. Mode-selectivity of the longest dynamics suggests coupling of the N–N and axial NO2 stretching modes with the long-lived, excited phonon bath.

Biological materials have attracted a lot of attention due to their simultaneous superior stiffness and toughness, which are conventionally attributed to their staggered structure (also known as brick and mortar) at the most elementary nanoscale level and self-similar hierarchy at the overall level. Numerous theoretical, numerical, and experimental studies have been conducted to determine the mechanism behind the load-bearing capacity of the staggered structure, while few studies focus on whether the staggered structure is globally optimal in the entire design space at the nanoscale level. Here, from the view of structural optimization, we develop a novel long short-term memory (LSTM) based iterative strategy for optimal design to demonstrate the simultaneous best stiffness and toughness of the staggered structure. Our strategy is capable of both rapid discovery and high accuracy based on less than 10% of the entire design space. Besides, our strategy could obtain and maintain all of the best sample configurations during iterations, which can hardly be done by the convolutional neural network (CNN)-based optimal strategy. Moreover, we discuss the possible future material design based on the failure point of the staggered structure. The LSTM-based optimal design strategy is general and universal, and it may be employed in many other mechanical and material design fields with the premise of conservation of mass and multiple optimal sample configurations.

Water splitting driven by renewable energy sources is considered a sustainable way of hydrogen production, an ideal fuel to overcome the energy issue and its environmental challenges. The rational design of electrocatalysts serves as a critical point to achieve efficient water splitting. Layered double hydroxides (LDHs) with two-dimensionally (2D) layered structures hold great potential in electrocatalysis owing to their ease of preparation, structural flexibility, and tenability. However, their application in catalysis is limited due to their low activity attributed to structural stacking with irrational electronic structures, and their sluggish mass transfers. To overcome this challenge, attempts have been made toward adjusting the morphological and electronic structure using appropriate design strategies. This review highlights the current progress made on design strategies of transition metal-based LDHs (TM-LDHs) and their application as novel catalysts for oxygen evolution reactions (OERs) in alkaline conditions. We describe various strategies employed to regulate the electronic structure and composition of TM-LDHs and we discuss their influence on OER performance. Finally, significant challenges and potential research directions are put forward to promote the possible future development of these novel TM-LDHs catalysts.

In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.

Structural disorder has been shown to be responsible for profound changes of the interaction-energy landscapes and collective dynamics of two-dimensional (2D) magnetic nanostructures. Weakly-disordered 2D ensembles have a few particularly stable magnetic configurations with large basins of attraction from which the higher-energy metastable configurations are separated by only small downward barriers. In contrast, strongly-disordered ensembles have rough energy landscapes with a large number of low-energy local minima separated by relatively large energy barriers. Consequently, the former show good-structure-seeker behavior with an unhindered relaxation dynamics that is funnelled towards the global minimum, whereas the latter show a time evolution involving multiple time scales and trapping which is reminiscent of glasses. Although these general trends have been clearly established, a detailed assessment of the extent of these effects in specific nanostructure realizations remains elusive. The present study quantifies the disorder-induced changes in the interaction-energy landscape of two-dimensional dipole-coupled magnetic nanoparticles as a function of the magnetic configuration of the ensembles. Representative examples of weakly-disordered square-lattice arrangements, showing good structure-seeker behavior, and of strongly-disordered arrangements, showing spin-glass-like behavior, are considered. The topology of the kinetic networks of metastable magnetic configurations is analyzed. The consequences of disorder on the morphology of the interaction-energy landscapes are revealed by contrasting the corresponding disconnectivity graphs. The correlations between the characteristics of the energy landscapes and the Markovian dynamics of the various magnetic nanostructures are quantified by calculating the field-free relaxation time evolution after either magnetic saturation or thermal quenching and by comparing them with the corresponding averages over a large number of structural arrangements. Common trends and system-specific features are identified and discussed.

In this study, we demonstrate the feasibility of Bi-doped tetrahedrite Cu12Sb4−xBixS13 (x = 0.02–0.20) synthesis in an industrial eccentric vibratory mill using Cu, Sb, Bi and S elemental precursors. High-energy milling was followed by spark plasma sintering. In all the samples, the prevailing content of tetrahedrite Cu12Sb4S13 (71–87%) and famatinite Cu3SbS4 (13–21%), together with small amounts of skinnerite Cu3SbS3, have been detected. The occurrence of the individual Cu-Sb-S phases and oxidation states of bismuth identified as Bi0 and Bi3+ are correlated. The most prominent effect of the simultaneous milling and doping on the thermoelectric properties is a decrease in the total thermal conductivity (κ) with increasing Bi content, in relation with the increasing amount of famatinite and skinnerite contents. The lowest value of κ was achieved for x = 0.2 (1.1 W m−1 K−1 at 675 K). However, this sample also manifests the lowest electrical conductivity σ, combined with relatively unchanged values for the Seebeck coefficient (S) compared with the un-doped sample. Overall, the lowered electrical performances outweigh the benefits from the decrease in thermal conductivity and the resulting figure-of-merit values illustrate a degradation effect of Bi doping on the thermoelectric properties of tetrahedrite in these synthesis conditions.