Three poly(organosiloxanes) (hydromethyl-, dimethyl-, and epoxymethylsiloxane) of different chain lengths and pendant groups and their mixtures of dimethyl (DMC) or diethyl carbonates (DEC) were applied in the modification of fumed silica nanoparticles (FSNs). The resulting modified silicas were studied in depth using 29Si, 1H, and 13C solid-state NMR spectroscopy, elemental analysis, and nitrogen adsorption-desorption (BET) analysis. The obtained results reveal that the type of grafting, grafting density, and structure of the grafted species at the silica surface depend strongly on the length of organosiloxane polymer and on the nature of the “green” additive, DMC or DEC. The spectral changes observed by solid-state NMR spectroscopy suggest that the major products of the reaction of various organosiloxanes and their DMC or DEC mixtures with the surface are D (RR’Si(O0.5)2) and T (RSi(O0.5)3) organosiloxane units. It was found that shorter methylhydro (PMHS) and dimethylsiloxane (PDMS) and their mixtures with DMC or DEC form a denser coverage at the silica surface since SBET diminution is larger and grafting density is higher than the longest epoxymethylsiloxane (CPDMS) used for FSNs modification. Additionally, for FSNs modified with short organosiloxane PMHS/DEC and also medium organosiloxane PDMS/DMC, the dense coverage formation is accompanied by a greater reduction of isolated silanols, as shown by solid-state 29Si NMR spectroscopy, in contrast to reactions with neat organosiloxanes. The surface coverage at FSNs with the longest siloxane (CPDMS) greatly improves with the addition of DMC or DEC. The data on grafting density suggest that molecules in the attached layers of FSNs modified with short PMHS and its mixture of DMC or DEC and medium PDMS and its mixture of DMC form a “vertical” orientation of the grafted methylhydrosiloxane and dimethylsiloxane chains, in contrast to the reaction with PDMS/DEC and epoxide methylsiloxane in the presence of DMC or DEC, which indicates a “horizontal” chain orientation of the grafted methyl and epoxysiloxane molecules. This study highlights the major role of solid-state NMR spectroscopy for comprehensive characterization of solid surfaces.

Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900–1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE–ELM model performed better, having a correlation coefficient of prediction (r_p), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE–ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

A passively mode-locked fiber laser with controllable pulse width is demonstrated by use of an all-fiber saturable absorber based on a hybrid no-core fiber (NCF)–graded index multimode fiber (GIMF) structure incorporated into an Er-doped fiber ring cavity. Such a hybrid NCF-GIMF structure has a tunable intracavity filtering effect. As a result, the mode-locking operation is achieved with controllable pulse width and spectral bandwidth in the normal dispersion regime by only stretching the fiber device. Soliton pulses with the pulse width of 7.7–23 ps are generated, and bound solitons with variable widths are also experimentally demonstrated. The results obtained reveal the versatility and flexibility of the NCF-GIMF structured device in controlling the pulse dynamics for different experimental requirements.

A universal multi-parameter sensing scheme based on a self-interference micro-ring resonator (SIMRR) is proposed. Benefit from the special intensity sensing mechanism, the SIMRR allows multimode sensing in a wide range of wavelengths but immune from frequency noise. To process the multiple mode spectra that are dependent on multiple parameters, we adopt the machine learning algorithm instead of massive asymptotic solutions of resonators. Employing the proposed multi-mode sensing approach, a two-parameter SIMRR sensor is designed. Assuming that two gases have different wavelength dependence of refractive indices, the feasibility and effectiveness of the two-parameter sensing strategy are verified numerically. Moreover, the dependence of parameter estimation accuracy on the laser intensity noises is also investigated. The numerical results indicate that our scheme of multi-parameter sensing in a multimode SIMRR holds great potential for practical high-sensitive sensing platforms compared with the single-mode sensing based on whispering gallery mode (WGM) resonators.

Organosilicon layers chemically anchored on silica surfaces show high carbon content, good thermal and chemical stability and find numerous applications as fillers in polymer systems, thickeners in dispersing media, and as the stationary phases and carriers in chromatography. Methyl-terminated poly(dimethylsiloxanes) (PDMSs) are typically considered to be inert and not suitable for surface modification because of the absence of readily hydrolyzable groups. Therefore, in this paper, we report a new approach for surface modification of silica (SiO₂) nanoparticles with poly(dimethylsiloxanes) with different lengths of polymer chains (PDMS-20, PDMS-50, PDMS-100) in the presence of diethyl carbonate (DEC) as initiator of siloxane bond splitting. Infrared spectroscopy (IR), elemental analysis (CHN), transmission electron microscopy (TEM), atomic force microscopy (AFM), rotational viscosity and contact angle of wetting were employed for the characterization of the raw fumed silica and modified silica nanoparticles. Elemental analysis data revealed that the carbon content in the grafted layer is higher than 8 wt % for all modified silicas, but it decreases significantly after sample treatment in polar media for silicas which were modified using neat PDMS. The IR spectroscopy data indicated full involvement of free silanol groups in the chemisorption process at a relatively low temperature (220 °C) for all resulting samples. The contact angle studies confirmed hydrophobic surface properties of the obtained materials. The rheology results illustrated that fumed silica modified with mixtures of PDMS-x/DEC exhibited thixotropic behavior in industrial oil (I-40A), and exhibited a fully reversible nanostructure and shorter structure recovery time than nanosilicas modified with neat PDMS. The obtained results from AFM and TEM analysis revealed that the modification of fumed silica with mixtures of PDMS-20/DEC allows obtaining narrow particle size distribution with uniform dispersity and an average particle size of 15–17 nm. The fumed silica nanoparticles chemically modified with mixtures of PDMS-x/DEC have potential applications such as nanofillers of various polymeric systems, thickeners in dispersing media, and additives in coatings.

Link prediction is the most basic and essential problem in complex networks. This study analyzes the observed topological, time, attributive, label, weight, directional, and symbolic features and auxiliary information to find the lack of connection and predict the future possible connection. For discussion and analysis of the evolution of the network, the network model is of great significance. In the past two decades, link prediction has attracted extensive attention from experts in various fields, who have published numerous high-level papers, but few combine interdisciplinary characteristics. This survey analyzes and discusses the existing link prediction methods. The idea of stratification is introduced into the classification system of link prediction for the first time and proposes the design idea of a seven-layer model, namely the network, metadata, feature classification, selection input, processing, selection, and output layers. Among them, the processing layer divides link prediction methods into similarity-based, probabilistic, likelihood, supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning methods. The input features, evaluation metrics, complex analysis, experimental comparisons, relative merits, common dataset and open-source implementations for each link prediction method are then discussed in detail. Through analysis and comparison, we found that the link prediction method based on graph structure features has better prediction performance. Finally, the future development direction of link prediction in complex networks is discussed.