In this paper, a grey prediction model group is employed to quantitatively study the impact of COVID-19 on natural gas consumption in Chongqing, China. First, a grey prediction model group suitable for the prediction of Chongqing's natural gas consumption is introduced, which consists of GM(1,1), TWGM(1,1), and the newly-developed ODGM(1,1). Then, the model group is constructed to predict Chongqing's natural gas consumption in 2020. Finally, compare the predicted results of the model group with the actual consumption and quantitatively analyze the impact of the epidemic on natural gas in Chongqing. It is found that the impact of the epidemic on the consumption of natural gas in the first quarter of the year is very small, but relatively bigger in the second and third quarters. The study is of positive significance to maintain the supply and demand balance of natural gas consumption in Chongqing in the background of COVID-19; and it enriches and develops the theoretical system of grey prediction models.

Organic photovoltaics relies on the development of stable chromophores and redox-active organic molecules with tailor-made HOMO/LUMO energies. Here, we present the synthesis and properties of novel dyads composed of boron subphthalocyanine (SubPc) and triangulene units, connected either at the peripheral position of the subphthalocyanine or at the axial boron. The connectivity has strong implications for the absorption and fluorescence properties of the dyads, as well as their redox properties. While the SubPc unit has a bowl shape, triangulene is a planar structural unit that allows dyads to dimerize in the solid state on account of π-stacking interactions as shown by X-ray crystallography of one of the dyads. The electronic properties were also studied computationally by density functional theory methods. Excellent agreement between experimental and computed data were obtained, showing that our computational method is a strong tool in the rational design of optimum molecules to ultimately obtain finely tuned molecules for device applications.

As interest in Internet-of-Things (IoT) devices like wireless sensors increases, research efforts have focused on finding ways for these sensors to self-harvest energy from the environment in which they are installed. Photovoltaic (PV) cells or mini-modules are an intuitive choice for harvesting indoor ambient light, even under low light conditions, and using it for battery charging and powering of these devices. Characterizations of battery charging, for small rechargeable batteries from low charge to full charge, have been investigated using PV mini-modules of equal area. We present battery charging results using three different PV technologies, monocrystalline silicon (c-Si), gallium-indium-phosphide (GaInP) and gallium-arsenide (GaAs) under a warm color temperature (3000 K) LED lighting at an illuminance of 1000 lx. Battery charging times are shortest for the more efficient GAInP and GaAs mini-modules whose spectral response are a better match to the LED test source, which contains mostly visible photons, and longest for the less efficient Si cells. As a demonstration, a wireless temperature sensor mote was attached to the charging circuit and operated to determine its power consumption in relation to the available charging power. The mote's maximum power draw was less than the charging power from the least efficient c-Si mini-module. Our findings affirm the feasibility of utilizing PV under typical indoor lighting conditions to power IoT devices.

Despite the recent discoveries of considerable fossil fuel reserves, Brazil is one of the only great economic and industrial powers with very high amounts of renewable energy in its electricity matrix. Approximately 79.3% of the electric energy supply comes from renewable resources, of which hydroelectric power represents 70.6%. The two primary concerns regarding hydroelectricity are the damage caused to the environment by the construction of dams and the uncertainty of the supply in cases of long drought seasons. This article presents an analysis on the availability and energy exploitation of sugarcane straw and forest residues derived from eucalyptus for decentralized generation using a Geographic Information System-based model. The potential bioelectricity and bioethanol production from sugarcane and eucalyptus biomass in the Administrative Region of Campinas (ARC) is higher than the demand in this region. The results provide guidelines for designing alternatives to the intended Nationally Determined Contributions in Brazil within the scope of the ARC, and they can be used to provide energy empowerment, electric matrix diversification, and new policies that address the residue availability and demand.

Spectral response measurements of germanium-based triple-junction solar cells were performed under a variety of light and voltage bias conditions. Two of the three junctions exhibited voltage and light bias dependent artifacts in their measured responses, complicating the true spectral response of these junctions. To obtain more insight into the observed phenomena, a set of current-voltage measurement combinations were also performed on the solar cells under identical illumination conditions, and the data were used in the context of a diode-based analytical model to calculate and predict the spectral response behavior of each junction as a function of voltage. The analysis revealed that both low shunt resistance and low breakdown voltages in two of the three junctions influenced the measured quantum efficiency of all three junctions. The data and the modeling suggest that combination of current-voltage measurements under various light bias sources can reveal important information about the spectral response behavior in multijunction solar cells.

We calibrate the seven parameters of a single-diode model (SDM) for photo-voltaic device performance using current-voltage (I-V) curves measured under controlled laboratory conditions over a matrix of nominal temperature and irradiance combinations. As described in previous modeling work, we do not use a short-circuit temperature coefficient parameter, which depends on the often unknown insolation spectrum and whose validity may be questionable. Alternatively, we employ a rigorous temperature-dependent extension of the spectral mismatch correction. This standard correction is routinely used by calibration laboratories to measure an (i.e., a particular ratio of short-circuit currents) using a calibrated reference device, thereby compensating for spectral effects of the irradiance and for any difference in spectral response between the test device and reference device. The calibrated SDM predicts the device's current at any prescribed voltage, temperature, and effective irradiance, and thus can predicts power and energy production under prescribed conditions. Our approach aligns well with the matched reference cell approach to outdoor I-V curve measurements, while clarifying the requirements of a "matched" condition for the irradiance monitoring device(s). We find evidence for significant model discrepancy in the SDM, suggesting that model improvements and measurement intercomparisons are needed.