Large-Area Layered Membranes with Precisely Controlled Nano-Confined Channels.

Two-dimensional (2D) nanosheets-based membranes, which have controlled 2D nano-confined channels, are highly desirable for molecular/ionic sieving and confined reactions. However, it is still difficult to develop an efficient method to prepare large-area membranes with high stability, high orientation, and accurately adjustable interlayer spacing. Here, we present a strategy to produce metal ion cross-linked membranes with precisely controlled 2D nano-confined channels and high stability in different solutions using superspreading shear-flow-induced assembly strategy. For example, membranes based on graphene oxide (GO) exhibit interlayer spacing ranging from 8.0 ± 0.1 Å to 10.3 ± 0.2 Å, with a precision of down to 1 Å. At the same time, the value of the orientation order parameter (f) of GO membranes is up to 0.95 and GO membranes exhibit superb stability in different solutions. The strategy we present, which can be generalized to the preparation of 2D nano-confined channels based on a variety of 2D materials, will expand the application scope and provide better performances of membranes.

Large-Area Ultrastrong and Stiff Layered MXene Nanocomposites by Shear-Flow-Induced Alignment of Nanosheets.

To shield increasingly severe radiation pollution, ultrathin MXene-based electromagnetic interference (EMI) shielding materials with excellent mechanical properties are urgently demanded in wearable electrical devices or aerospace fields. However, it is still a challenge to fabricate ultrastrong and stiff MXene-based nanocomposites with excellent EMI shielding capacity in a universal and scalable manner. Here, inspired by the natural nacre structure, we propose an efficient superspreading strategy to construct a highly oriented layered "brick-and-mortar" structure using shear-flow-induced alignment of MXene nanosheets at an immiscible hydrogel/oil interface. A continuous and large-area MXene nanocomposite film has been fabricated through a homemade industrial-scale continuous fabrication setup. The prepared MXene nanocomposite films exhibit a tensile strength of 647.6 ± 56 MPa and a Young's modulus of 59.8 ± 6.1 GPa, respectively. These outstanding mechanical properties are attributed to the continuous interphase layer that formed between the well-aligned MXene nanosheets. Moreover, the obtained MXene nanocomposites also show great EMI shielding effectiveness (51.6 dB). We consider that our MXene-based nanocomposite films may be potentially applied as electrical or aerospace devices with superior mechanical properties and high EMI shielding capacity.

[Research on predicting the qualities of stem of alfalfa hay by near infrared reflectance spectroscopy].

The present research was attempted to predict the qualities of stem of alfalfa (Medicago sativa L. ) without separation from the whole plant by near infrared reflectance spectroscopy and discussed the feasibility of using the near infrared reflectance spectra information of the whole object to predict the qualities of a certain part. Sixty six whole alfalfa hay samples of separated stems from leaves were collected and they were distinguishing by years, cultivars, cuts and growing periods. There were 138 calibration samples and 60 validation samplers. Fourier transform-near infrared reflectance spectroscopy (FT-NIRS) and partial least square (PLS) were used to set up the calibration models of stem's crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude ash (CA) and in vitro digestible dry matter (IVDDM) contents. All models showed great calibration and prediction performances except the one of stem's NDF content. The correlation coefficients of cross-validation (rCV) were between 0.8523 and 0.9007, the root mean square errors of cross-validation (RMSECV) were between 0.72% and 3.96% and the correlation coefficients of NIRS values and chemical values (r) were between 0.9255 and 0.9512. However, rCV, RMSECV and r of the model of stem's NDF content were 0.8214, 3.70% and 0.9020, respectively. It wasn't exact enough and would be used for rough predicting only. All of the results showed that near infrared reflectance spectra information of whole alfalfa hay could be used to predict some components of its stem exactly. It was the maiden attempt of using near infrared reflectance spectra information of the whole objects to evaluated the qualities of a certain part.

[Determination of main fiber compositions of alfalfa hay by near infrared reflectance spectroscopy].

Sixty alfalfa samples, with different growth stage, cultivars, and preparing method (drying by oven, shade and sun), were selected to study the potential of determination of cellulose, hemicellulose and lignin content in the present research. The result showed that the correlation coefficient of cross-validation (R(cv)), determination coefficient of external validation(r2) and the ratio of standard deviation to root mean square error of prediction (RPD) of cellulose and lignin were 0.97, 0.97 and 4.44, and 0.94, 0.94 and 4.08, respectively. This indicated the feasibility of determining cellulose and lignin content of alfalfa using near infrared reflectance spectroscopy. Hemicellulose was not predicted accurately by NIRS in this study, due to the lowest accuracy (R(cv) = 0.39, r2 = 0.29, RPD = 1.09). The exact determination of cellulose and lignin using near infrared reflectance spectroscopy will be useful to quality control in alfalfa production and quickly analyzing the fiber composition of alfalfa samples breeding research.

[Prediction of leaf concentration in alfalfa using near infrared reflectance spectroscopy].

Leaf concentration in alfalfa is an important factor affecting the nutritive value, forage intake and digestibility. Estimates of leaf concentrations commonly used currently involve a labor intensive process of hand separating leaf and stem fractions. In the present study, a total of 41 artificial alfalfa samples were mixed with different leaf concentrations ranging from 15% to 55%. The object was to develop 3 calibrations for predicting alfalfa leaf concentrations using 15, 25 and 35 calibrated samples by near infrared reflectance spectroscopy. The root mean square error of prediction(RMSEP)was 1.02, 1.97 and 0.51, respectively. External validation had a coefficient of determination (r2) ranging from 0.79 8 to 0.998 9. The ratio of performance to standard deviation (RPD) varied from 2.85 to 25.93. The results showed that 15 samples could develop accurate NIRS model of alfalfa leaf concentrations; the calibration equations got better accuracy with the increase in calibrated samples numbers from 15 to 35.

[Research on predicting the nutrition value of pelletized alfalfa by near infrared reflectance spectroscopy].

The present research aimed to predict the qualities of pelletized alfalfa by near infrared reflectance spectroscopy. Sixty pelletized alfalfa samples were collected, including 22 whole plant alfalfa samples, 19 stem samples and 19 leaf samples. They were divided into a calibration sample set (45 samples) and a validation sample set (15 samples). The Fourier transform-near infrared reflectance spectroscopy (FT-NIRS) and the partial least square (PLS) were used to calibrate models of the pelletized alfalfa nutrition value, involving crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents. All models had great calibration performances. The correlation coefficients of cross-validation (R(CV)) were between 0.96410 and 0.96887, and the root mean square errors of cross-validation (RMSECV) were between 0.80% and 2.59%. Fifteen validation samples were used to predict the performances of these models, all the correlation coefficients of NIRS value and chemical value (r) were between 0.9669 and 0.9743, and the root mean square errors of prediction (RMSEP) were between 0.85% and 2.07%. The RPD values of cross-validation and prediction were all above 3. The results showed that pelletized alfalfa's CP, NDF, ADF contents were exactly predicted by near infrared reflectance spectroscopy.

[Quality prediction of alfalfa hay using Fourier transform near infrared reflectance spectroscopy].

Alfalfa hay has high nutritive value, and it is one of the most important protein feed for domestic animals. The quality parameters of alfalfa hay, including CP, Ash, NDF, ADF, ADL and IVDMD, were predicted using Fourier transform near infrared reflectance spectroscopy with PLS regression in this test. Then the 6 models were validated by cross-validation and external-validation. The results indicated that FT-NIR models of alfalfa hay quality have considerable accuracy and precision: the correlation coefficient of cross-validation is 0.953 88 to 0. 990 19, and the RMSECV is 1.980-0.345; The correlation coefficient of external-validation is 0.963-0. 990. By using FT-NIR, analysis can rapidly and accurately determine the quality of alfalfa without any chemical reagent. This method is of great significance for analysing the trait of alfalfa production, the quality determination, the estimation of germ plasm resource, and the identifying and selecting of hybridized generations in alfalfa research of China.

[Applications of near infrared reflectance spectroscopy technique (NIRS) to grassland ecology research].

The technology of near infrared reflectance spectroscopy (NIRS) have been widely used in many research areas, owing to its rapidness, high efficiency, low cost and no pollution. The present paper mainly illustrates the significance of the applications of NIRS to grassland ecology research, and explains some innovative implications of near infrared reflectance spectroscopy in the determination of a variety of forage nutrients, minerals, and the components of soil, prediction of the composition of for-age mixtures, animal performance, grass resistance of diseases and insect pests and other complex ecological characters, and doing research on biochemical markers, isotope discrimination and so on. By synthesizing these applications properly, it is concluded that NIRS could be used as a holistic tool in grassland ecology research to determine the chemical components, and analyze the complex dynamic character of grassland ecosystem and the total specialty of the system running. According to this paper, the authors also hope to promote the application conditions of NIRS in the grassland ecology research in China, and accelerate the modernization of research measures in this area.