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This article presents an improved deadbeat indirect torque control (ITC) method for switched reluctance motors (SRMs) with the primary goal of reducing torque ripple. The proposed control approach comprises two parts: a torque-to-current conversion scheme that the proposed method achieves excellent current and a deadbeat controller (DBC). In the conversion scheme, a second-order SRM Fourier-series model is constructed by integrating the current vector decomposition method. Subsequently, an iterative learning controller (ILC) is designed based on this model to achieve precise conversion from the electromagnetic torque to the q-axis current, which eliminates the need for additional modeling processes. Within the proposed DBC controller, a novel recursive least squares (RLS) estimator is introduced to effectively tackle the issue of model variations. This integration enables the adaptive calibration of the predictive model, ultimately guaranteeing optimal performance in the current control. Furthermore, the consistency of the model employed in both the DBC and conversion scheme empowers the RLS to further refine the accuracy of torque-to-current conversion, thereby improving torque ripple suppression performance. Comparative experiments are conducted on a 12/8 SRM to evaluate the proposed control method’s performance. The experimental results show that the proposed method achieves excellent current tracking and torque ripple suppression performance in SRM drives.

The ongoing advancement of optical devices and the intensive research on the infrared spectrum are driving a revolutionary transformation in infrared digital holography (IRDH) technology. Currently, various imaging methods are applied in diverse fields, such as nondestructive testing, medical imaging, and materials science, including visible light digital holography, X-ray, and computed tomography imaging. However, due to the complex structures of matter and the characteristics of biological tissues, traditional imaging methods have limitations in providing comprehensive and accurate information. IRDH with its longer wavelength and stronger penetration capability, has shown advantages in nondestructive testing, medical imaging, and materials science, and can provide more comprehensive and accurate information compared with visible light digital holography. This study explores the advancements in IRDH technology, with a focus on the development of optical devices and the enhancement of image quality through advanced image processing algorithms. It underscores the critical role of IRDH in nondestructive testing and biomedical applications, providing practical examples, such as testing aerospace materials, detecting retinal pathologies, and diagnosing eye diseases. In addition, this article offers a comprehensive overview of the key application areas, current research directions, and future development trends of IRDH technology.

In order to efficiently obtain the optimum hybrid extraction solvents for mercaptan removal from liquefied petroleum gas, the method of combining molecular dynamics simulation preselection and extraction experiment was carried out. The results show that the selected hybrid solvents all exhibit a better extraction desulfurization performance, and of which, the isopropanol hybrid (IPA) solvent exhibited the best removal efficiency. With the aid of density functional theory calculations, the interaction mechanism was studied and the results show that the weak hydrogen bond plays a crucial role in the n-propylmercaptan (PM) removal process. The strength of the hydrogen bond is attributed to the structure of solvents, such as functional groups as the acceptor of the hydrogen bond and the species, number, and length of side chains attached to functional groups. Based on the above mentioned analysis, IPA exhibits a stronger hydrogen bond interaction with PM, which significantly enhances the extraction capacity and prolongs the service life of the solvent.

This investigation presents a novel approach for the nondestructive, and real-time analysis of crystalline structures, including transition metal dichalcogenides renowned for their optoelectronic capabilities. The methodology employs a synergistic blend of infrared digital holography and deep learning, utilizing an in-line system and Transformer-based deep learning algorithms, to provide detail in the material microstructure. The article investigates the effects of different parameters on reproduction fidelity, with a particular focus on phase accuracy. A holography-guided training strategy is proposed to enhance the framework’s performance. By demonstration applications such as evaluating the dielectric characteristics of ReS2, detecting material thickness layers in MoS2, and monitoring the microstructure evolution during the growth of NaCl and CuSO4crystals. Not only addresses existing limitations in material characterization but also offers avenues for exploration.

Investigating the crystallization process is a crucial approach for understanding crystal properties. However, traditional optical microscopy and infrared spectroscopy often fail to capture essential information, particularly during the crystallization behavior, due to common limitations in the field of view and the inability to obtain phase features. To address these challenges, a novel approach is proposed that combines a free field of view scheme with infrared holographic detection for dynamic crystal observation. This configuration enables the acquisition of phase attributes of targets in a field of view. We analyze the issue of displacement errors in reconstruction and overcome it by employing a subpixel displacement registration and intensity calibration algorithm. Experimental verification demonstrates that the present approach can not only be applied to the study of crystallization processes but also enables the amplitude and phase characteristics of steady resolution targets, artificial samples, natural objects, and minerals in the infrared band. Infrared digital holography using the free field of view, ranging from 1.2 million pixels to 55 million pixels, is expected to be a promising technical tool in the research of crystallography and mineral materials identification, especially in the blind spot of traditional detection techniques, where more detailed properties can be obtained through phase information.

The nickel hydroxides (β-Ni(OH)2) with different shapes such as hexagonal nanosheets, irregular nanosheets, and nanoparticles were synthesized in the absence of SO42−ions and in the presence of 0.00−3.00 mmol of the added NaOH using a hydrothermal method. The β-Ni(OH)2phase with brucite-type structure was confirmed by an energy-dispersive spectrum (EDS), Fourier transform infrared spectra (FTIR), and powder X-ray diffraction (XRD). The effect of the free OH−ions and the SO42−ions in the hydrothermal system on morphologies of the Ni(OH)2products was investigated in detail. In the absence of SO42−ions system, when the added NaOH amount is less than 0.02 mmol, the irregular thin nickel hydroxide nanosheets with thickness of about 20−50 nm were obtained; when the added NaOH amount is between 0.35 and 0.55 mmol, the products have the regular hexagonal morphology with a width of 150−500 nm and thickness of 40−80 nm; while when the added NaOH amount is 1.50−3.00 mmol, the nickel hydroxide products became polyhedral nanoparticles with an average diameter of ca. 50−90 nm. The Ni(SO4)0.3(OH)1.4nanowires or α-Ni(OH)2nanowires containing the intercalated SO42−anions can only be obtained in the presence of SO42−ions. They have lengths of several micrometers and widths of 20−30 nm. A possible growth mechanism of the nanosheets, nanoparticles, and nanowires is suggested.