Polymorph transformation in a mixed-stacking nickel-dithiolene complex with the derivative of 4,4'-bipyridinium.

In this study, two polymorphs of the [1,1'-dibutyl-4,4'-bipyridinium][Ni(mnt)2] salt (1) were synthesized. The dark-green polymorph (designated as 1-g) was prepared under ambient conditions by the rapid precipitation method in aqueous solutions. Subsequently, the red polymorph (labeled as 1-r) was obtained by subjecting 1-g to ultrasonication in MeOH at room temperature. Microanalysis, infrared spectroscopy, thermogravimetry (TG), differential scanning calorimetry (DSC), and powder X-ray diffraction (PXRD) techniques were used to characterize the two polymorphs. Both 1-g and 1-r exhibit structural phase transitions: a reversible phase transition at ∼403 K (∼268 K) upon heating and 384 K (∼252 K) upon cooling for 1-g (1-r) within the temperature range below 473 K. Interestingly, on heating 1-r to 523 K, an irreversible phase transition occurred at about 494 K, resulting in the conversion of 1-r into 1-g. Relative to 1-r, 1-g represents a thermodynamically metastable phase wherein numerous high-energy conformations in butyl chains of cations are confined within the lattice owing to quick precipitation or rapid annealing from higher temperatures. Through variable-temperature single crystal and powder X-ray diffractions, UV-visible spectroscopy, dielectric spectroscopy, and DSC analyses, this study delves into the mechanism underlying phase transitions for each polymorph and the manual transformation between 1-g and 1-r as well.

Thermochromism of rapid responses to temperature changes versus mechanochromism in a Ni-dithiolene complex salt.

A thermochromic or mechanochromic material can switch between at least two stable states in response to changes in temperature or static pressure/strain. In this study, we investigated a Ni-dithiolene dianion salt, 1,1'-diheptyl-4,4'-bipyridinium bis(maleonitriledithiolato)nickelate (1), and found that its cations and anions stack alternately to form a uniform mixed stack. These mixed stacks then combine to form a molecular solid through Coulomb and van der Waals interactions. Upon heating, 1 undergoes a reversible phase transition at around 340/320 K during the first heating/cooling cycle, resulting in rapid thermochromism with a color change from green (stable state) to red (metastable state) within a few seconds. This is the first report of a crystal of bis(maleonitriledithiolato)nickelate(II) salt with green color. Additionally, 1 exhibits irreversible mechanochromism, intense near-IR absorbance, and a dielectric anomaly. The structural phase transition is responsible for these properties, as it induces alterations in the π-orbital overlap between the anion and cation within a mixed stack. The intense near-IR absorbance arises from the ion-pair charge transfer transition from [Ni(mnt)2]2- to 4,4'-bipyridinium.

Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.

Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky-Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS-SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R 2 = 0.741-0.852), while the SVR and PLSR models performed similarly (R 2 = 0.733-0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R 2 was 0.849-0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R 2 increased by 14.39%, and the R 2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.

Estimations of Water Use Efficiency in Winter Wheat Based on Multi-Angle Remote Sensing.

Real-time non-destructive monitoring of water use efficiency (WUE) is important for screening high-yielding high-efficiency varieties and determining the rational allocation of water resources in winter wheat production. Compared with vertical observation angles, multi-angle remote sensing provides more information on mid to lower parts of the wheat canopy, thereby improving estimates of physical and chemical indicators of the entire canopy. In this study, multi-angle spectral reflectance and the WUE of the wheat canopy were obtained at different growth stages based on field experiments carried out across 4 years using three wheat varieties under different water and nitrogen fertilizer regimes. Using appropriate spectral parameters and sensitive observation angles, the quantitative relationships with wheat WUE were determined. The results revealed that backward observation angles were better than forward angles, while the common spectral parameters Lo and NDDAig were found to be closely related to WUE, although with increasing WUE, both parameters tended to become saturated. Using this data, we constructed a double-ratio vegetation index (NDDAig/FWBI), which we named the water efficiency index (WEI), reducing the impact of different test factors on the WUE monitoring model. As a result, we were able to create a unified monitoring model within an angle range of -20-10°. The equation fitting determination coefficient (R 2) and root mean square error (RMSE) of the model were 0.623 and 0.406, respectively, while an independent experiment carried out to test the monitoring models confirmed that the model based on the new index was optimal, with R 2, RMSE, and relative error (RE) values of 0.685, 0.473, and 11.847%, respectively. These findings suggest that the WEI is more sensitive to WUE changes than common spectral parameters, while also allowing wide-angle adaptation, which has important implications in parameter design and the configuration of satellite remote sensing and UAV sensors.