2DCOS and I. Three decades of two-dimensional correlation spectroscopy

Temperature-Controlled Chain Dynamics in Polyimide Doped with CoCl2 Probed Using Dynamic Mechanical Analysis

Cobalt(II) chloride (CoCl2) being in the vicinity of polyimide chains entails modifications in terms of the molecular dynamics, which are mainly governed by the possible presence of amic acid residual groups, by the transition-metal-type characteristics of cobalt and by the CoCl2 content. Polyimide was synthesized using poly(amic acid) according to the reaction of 2,2'-bis(3,4-dicarboxylphenyl)hexafluoropropane dianhydride (6FDA) with 3,3'-dimethyl-4,4'-diaminodiphenylmethane (MMDA) in N,N-dimethylacetamide. CoCl2 was added before the thermal imidization of the poly(amic acid). An experimental approach was designed to establish the interaction between the polyimide and CoCl2 and whether the interaction depends on the quantity of the salt. Evidence for the existence of residual amic acid groups was obtained using second derivative Fourier Transform Infrared Spectroscopy (FTIR) and with the help of 2D correlation spectroscopy (2D-COS). Moreover, FTIR, along with X-ray photoelectron spectroscopy (XPS), revealed the interaction between the polymer and CoCl2, primarily in the form of Co(II)-N coordinated bonds. Nevertheless, the coordination of cobalt with suitable atoms from the amic acid groups is not precluded. The results of dynamic mechanical analysis (DMA) featured a specific relaxation assigned to the presence of CoCl2 in the polymeric film and demonstrated that its (non)reinforcing effect depends on its content in the polyimide.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

Two-dimensional correlation spectroscopy indicates the infrared spectral markers of the optimum scorching degree of rhubarb (Rhei Radix et Rhizoma) to enhance the anti-inflammatory activity

Rhubarb has been used as herbal purgative with a long and worldwide history. But purgation is a side effect of rhubarb in many cases. In traditional Chinese medicine, rhubarb can be stir-baked to scorch to attenuate the purgative function while enhance other bioactivities such as the anti-inflammatory effect. However, the over-scorched rhubarb will lose necessary active ingredients and even generate toxic ingredients. Nowadays, the scorching degree of rhubarb is still determined by the intuitive observation of color changes in production. Therefore, this research was designed to develop more reasonable and objective criteria to evaluate the scorching degree to ensure the efficacy, safety and consistency of the scorched rhubarb. Taking the example of the rhubarb baked at 200 °C for different times, the combination of trait indicator (color), chemical indicator (combined and free AQs, sennosides, gallic acid, 5-HMF) and biological indicator (anti-inflammatory) showed that the rhubarb baked for 30 min can be treated as the right scorched. Two-dimensional correlation spectroscopy helped to reveal the infrared spectral markers of the water extract near 1694 cm^-1, 1442 cm^-1 and 825 cm^-1, as well as the relative strength of the absorption bands of the powder near 1610 cm^-1 and 1020 cm^-1, to discriminate the right-scorched and over-scorched rhubarb. In conclusion, the combination of trait, chemical and biological indicators can provide reasonable and objective criteria for the optimum scorching degree of rhubarb, while FTIR spectroscopy can assess the right endpoint of the scorching process in a rapid, cheap and green way.

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model's SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0-10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R 2 val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R 2 val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

Zwitterionic Skins with a Wide Scope of Customizable Functionalities

With growing interest in the fields of wearable devices, it is crucial yet rather challenging to develop skinlike soft conductive materials with customizable functionalities and human tissue-compatible mechanical properties. Previously reported electronic skins struggle to meet the demands for transparence, mechanical adaptability, and stable conductivity during deformation. The recent rise of ionic skins with inorganic salts or ionic liquids doping provides the intrinsic stretchability, however, dilemmas remain for their limited functionalities such as a monotonous appearance and a narrow scope of mechanical and sensory properties. Herein, we design a type of zwitterionic hydrogels from the perspective of molecular interactions, which successfully combines ultrastretchability (>10000% strain), high strength (∼300 kPa), self-healability (at room temperature within 12 h), 3D printability, distinct stimuli-responsibility, biocompatibility, and antibacterial activity. The wide spectrum of such excellent properties has been rarely reported before and along with the ability to fabricate bioinspired intelligent skins recreating multiple sensations and mechanical properties of human skin, covering a broad range of sensitivity, and displaying tunable visual effect. We believe this work will inspire the programming of stimuli-responsive skinlike materials and contribute to the smart devices for information transformation between natural and artificial interfaces.