Based on the important effect of carrier recombination on the photovoltaic efficiency of organic solar cells, the intermolecular carrier recombination dynamics in organic solar cells is studied theoretically by using an extended Su-Schrieffer-Heeger tight binding model combined with the non-adiabatic quantum dynamical method in this article. Firstly, intermolecular charge recombination dynamics of the positive and negative carriers at the donor/acceptor interface is simulated and revealed, and it is found that the intermolecular carrier recombination exhibits fractional charge recombination along with energy loss. Subsequently, influence of the system energy offset Δ, electric field, thermal effect and aggregation of acceptor molecules on intermolecular carrier recombination dynamics is studied. The results show that the system energy offset Δ exhibits the carrier recombination barrier, and the larger the energy offset Δ, the more favorable it is to suppress the recombination of carriers. The electric field can inhibit the recombination of carriers by inducing spatial delocalization of positive and negative charges. Especially, as the electric field is strong enough, it can dissociate the recombined charge transfer state into free carriers. Thermal effects can cause random fluctuations of the potential energy of the donor/acceptor material, which can reduce the recombination barrier of carriers, and further to aggravate the carrier recombination. The aggregation of acceptor molecules will induce the expansion of electrons between acceptor molecules, increasing the distance between positive and negative charge centers at the interface, thereby inhibiting the recombination of carriers.

The galvanic corrosion of aluminum alloy mainly depends on the potential difference between the second phase and matrix. The greater the potential difference, the stronger the corrosion driving force. Based on AlZr binary alloy, we calculated the work functions of the (100), (110) and (111) planes of 19 ternary precipitates L1₂-Al_xZr_yX_z(X=Pd, Pt, Au, K, Rb, Sr, Ba, Ca, Yb, La, Ce, Y, Er, Sc, Zr, Ti, Cd, Hf, In) comprehensively by using the first principle calculations. The relationship between the work functions and the electronegativity of doped atoms was analyzed, and the fundamental reason for the influence of doped atoms on the surface galvanic corrosion performance of L1₂ type Al-Zr-X ternary aluminum alloy is clarified from the electronic level. Through calculation, we find that when different doped crystal surfaces are exposed to the surface, the potential difference between the phase and the matrix is also different due to the varying work functions. The doped atoms Hg, Cd, Zr, Ti, Hf can increase the work functions of the (100) surface of L1₂-Al_xZr_yX_z ternary precipitates, Hg, Cd, In, Ti, Hf can increase the work function of the (110) surface, and Pd, Pt, Au, In, Sc, Rb, Sr, Yb, Y, Er, K, Ba, La, Ce and Ca could decrease the work function of the (111) surface. These will lead to the further reduction of the potential difference between the phase and matrix. In addition, the linear positive correlation between the electronegativity of doped atoms and the work function of the compound is revealed. In contrast, In, Cd, Hg atoms whose electronegativity is close to Al and doping the site of Al, and Ti, Hf atoms whose electronegativity is close to Zr and doping the site of Zr have less influence on the work function of the phase, and the potential difference between their compounds and aluminum matrix is small, which is beneficial to improving the corrosion resistance of materials. Other doped atoms have greater influence on the work function of the phase. Research results have explained some of the experimental results of corrosion resistance studies, which provided a theoretical reference for optimizing the design of alloy composition and improving the corrosion resistance of aluminum alloy materials.

In this study, a mixed flow closed cooling tower was tested with a control variable method. The factors affecting outlet water temperature were screened with a grey correlation analysis method. Five most important factors were taken as input features in a gray_BP neural network which was developed to predict outlet water temperature of the mixed flow closed cooling tower. These factors include inlet water temperature, wet bulb temperature, water refill temperature, circulating water flow rate and air volume, and the prediction output is outlet water temperature. The network adopts a three-layer structure, four-hidden layer neurons, and 30 000 iterations. Experimental data that involve no training set were used to validate the developed model. It shows that the gray neural network model outperforms the traditional BP neural network model. The correlation coefficient, average relative error, root mean square error are 0.998 9, 0.293 4% and 0.152 9, respectively. We concluded that the gray_BP neural network is a promising algorithm for predicting water temperature of a mixed flow closed cooling tower.