Resolution Performance Research Articles

An Amphiphilic Star-Shaped Polymer (Star-PEG-PCL2) used as a Stationary Phase for GC.

A star-shaped polymer (Star-PEG-PCL2) was synthesized with PCL and PEG and used as a stationary phase for gas chromatography. The statically coated Star-PEG-PCL2 column exhibited an efficiency of 2260 plates/m determined by naphthalene at 120 °C and moderate polarity. The Star-PEG-PCL2 column showed high resolution performance for isomers of a wide ranging polarity, including methylnaphthalenes, halogenated benzenes, nitrobenzene, phenols, and anilines, and displayed dual-nature selectivity for a mixture of 17 analytes. Also, the Star-PEG-PCL2 column exhibited good separation performance and column inertness for Grob test mixture and a series of cis-/trans-isomers. In addition, it exhibited advantageous separation performance over the commercial HP-35 and PEG-20M columns for chloroaniline and bromoaniline isomers through its unique three-dimensional framework. In conclusion, it has good potential as a new stationary phase for separating a variety of analytes because of its special structure and excellent separation performance.

Ultra Performance Liquid Chromatography (UPLC) - A Review

Ultra performance liquid chromatography is a modern technique which gives a new direction for liquid chromatography. ultra performance liquid chromatography is a technique that improves in three key areas: "speed, resolution, and sensitivity.". when compared to high-performance liquid chromatography, ultra performance liquid chromatography is suited for particles with a diameter of less than 2 µm and can achieve superior resolution, speed, and sensitivity high performance liquid chromatography.

GrpAvoid: Multigroup Collision-Avoidance Control and Optimization for UAV Swarm.

Collision-avoidance control for UAV swarm has recently drawn great attention due to its significant implications in many industrial and commercial applications. However, traditional collision-avoidance models for UAV swarm tend to focus on avoidance at individual UAV level, and no explicit strategy is designed for avoidance among multiple UAV groups. When directly applying these models for multigroup UAV scenarios, the deadlock situation may happen. A group of UAVs may be temporally blocked by other groups in a narrow space and cannot progress toward achieving its goal. To this end, this article proposes a modeling and optimization approach to multigroup UAV collision avoidance. Specifically, group level collision detection and adaption mechanism are introduced, efficiently detecting potential collisions among different UAV groups and restructuring a group into subgroups for better collision and deadlock avoidance. A two-level control model is then designed for realizing collision avoidance among UAV groups and of UAVs within each group. Finally, an evolutionary multitask optimization method is introduced to effectively calibrate the parameters that exist in different levels of our control model, and an adaptive fitness evaluation strategy is proposed to reduce computation overhead in simulation-based optimization. The simulation results show that our model has superior performances in deadlock resolution, motion stability, and distance maintenance in multigroup UAV scenarios compared to the state-of-the-art collision-avoidance models. The model optimization results also show that our model optimization method can largely reduce execution time for computationally-intensive optimization process that involves UAV swarm simulation.

Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future “smart city” developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic conditions, in order for DAS to be a feasible technology in real-world scenarios, detection algorithms need to also perform robustly under a wide range of traffic conditions. In this study we investigate the potential of roadside DAS for the simultaneous detection and characterisation of the velocity of individual vehicles. To improve the temporal resolution and detection accuracy, we propose a self-supervised Deep Learning approach that deconvolves the characteristic car impulse response from the DAS data, which we refer to as a Deconvolution Auto-Encoder (DAE). We show that deconvolution of the DAS data with our DAE leads to better temporal resolution and detection performance than the original (non-deconvolved) data. We subsequently apply our DAE to a 24-hour traffic cycle, demonstrating the feasibility of our proposed method to process large volumes of DAS data, potentially in near-real time.

Evolutionary Optimization of Multiple Machine-Learned Objectives for PET Image Reconstruction

This article proposes optimizing machine-learned objective functions to estimate a set of images optimal for multiple tasks. Tomographic image reconstruction commonly relies on optimizing an objective function that dictates the goodness of an image estimate. We propose a novel use of deep learning in which we train models to perform blind estimation of the goodness of an image. The trained model that maps an image to a target figure of merit serves as the objective function. Using simulated PET images, we train convolutional neural networks (CNNs) to estimate for a single image the root mean square error (RMSE), signal-to-noise ratio, and full width half maximum (FWHM) of a point source. These figures of merit serve as surrogates for the common medical task performance in terms of quantitation, detection, and spatial resolution, respectively. These multiple objective functions are optimized using our recently proposed multiobjective evolutionary algorithm for PET image reconstruction. The evolutionary algorithm seeks to identify the Pareto-optimal front of image solutions, optimal for multiple objectives. Additionally, we propose a reference point method to focus the evolutionary optimization on regions of the multiobjective space to reduce computation. Likewise, we integrate optimality conditions to estimate how close a solution is to optimal without any prior knowledge about the Pareto-optimal front in an effort to guide termination conditions. The multiobjective approach is compared with conventional maximum-likelihood reconstruction with increasing explicit regularization and maximum a posteriori reconstruction with varying penalty strength. The approaches are tested with simulated PET data from a digital phantom and patient data. Results suggest that the proposed machine-learned objectives can estimate quantification, detection, and spatial resolution performance and that the evolutionary algorithm estimates a set of solutions with a good balance for convergence and diversity across different multiobjective combinations. In general, the evolutionary algorithm estimates images with superior performance compared to conventional techniques. Future work is needed to ensure and improve generalizability of the machine-learned objectives for different imaging conditions and to explore other target figures of merit for training machine-learned objectives. This proof-of-concept effort demonstrates a new use of deep learning for tomographic image reconstruction.

A conformal TOF-DOI Prism-PET prototype scanner for high-resolution quantitative neuroimaging.

Positron emission tomography (PET) has had a transformative impact on oncological and neurological applications. However, still much of PET's potential remains untapped with limitations primarily driven by low spatial resolution, which severely hampers accurate quantitative PET imaging via the partial volume effect (PVE). We present experimental results of a practical and cost-effective ultra-high resolution brain-dedicated PET scanner, using our depth-encoding Prism-PET detectors arranged along a compact and conformal gantry, showing substantial reduction in PVE and accurate radiotracer uptake quantification in small regions. The decagon-shaped prototype scanner has a long diameter of 38.5cm, a short diameter of 29.1 cm, and an axial field-of-view (FOV) of 25.5mm with a single ring of 40 Prism-PET detector modules. Each module comprises a 16 × 16 array of 1.5 × 1.5 × 20-mm3 lutetium yttrium oxyorthosillicate (LYSO) scintillator crystals coupled 4-to-1 to an 8 × 8 array of silicon photomultiplier (SiPM) pixels on one end and to a prismatoid light guide array on the opposite end. The scanner's performance was evaluated by measuring depth-of-interaction (DOI) resolution, energy resolution, timing resolution, spatial resolution, sensitivity, and image quality of ultra-micro Derenzo and three-dimensional (3D) Hoffman brainphantoms. The full width at half maximum (FWHM) DOI, energy, and timing resolutions of the scanner are 2.85 mm, 12.6%, and 271 ps, respectively. Not considering artifacts due to mechanical misalignment of detector blocks, the intrinsic spatial resolution is 0.89-mm FWHM. Point source images reconstructed with 3D filtered back-projection (FBP) show an average spatial resolution of 1.53-mm FWHM across the entire FOV. The peak absolute sensitivity is 1.2% for an energy window of 400-650 keV. The ultra-micro Derenzo phantom study demonstrates the highest reported spatial resolution performance for a human brain PET scanner with perfect reconstruction of 1.00-mm diameter hot-rods. Reconstructed images of customized Hoffman brain phantoms prove that Prism-PET enables accurate radiotracer uptake quantification in small brain regions (2-3 mm). Prism-PET will substantially strengthen the utility of quantitative PET in neurology for early diagnosis of neurodegenerative diseases, and in neuro-oncology for improved management of both primary and metastatic brain tumors.

Fast Sparse Non-Negative Least Squares via ADMM for High Resolution DOA Estimation

High-resolution and low-complexity direction of arrival (DOA) estimation deserves a warm welcome in a real radar sensor. However, achieving this goal is nontrivial. For the DOA estimation of the uniform linear array (ULA), we offer an efficient approach to reach a good compromise between complexity, resolution and accuracy. Under the covariance fitting framework, we construct a sparse non-negative least squares (NNLS) that is solved by the alternating direction method of multipliers (ADMM) at a low-computational complexity. The regularization item involved in the ADMM procedure yields an inherent improvement on robustness. Moreover, a large-scale matrix inversion that is a computationally expensive operation in the ADMM procedure is recursively computed by exploiting the diagonal matrix property of the regularization item, resulting in a reduction on complexity. In addition, the pre-estimation of the noise variance further reduces the scale of the sparse NNLS problem and alleviates the ill-conditioning effect to some extent. Compared to several sparse covariance fitting methods, both the simulation data and the 77 GHz radar-measured data demonstrate that the proposed algorithm has the good resolution, robustness, and accuracy performance with lower computationally cost.

A 200 MHz 14 W Pulsed Optical Illuminator With Laser Driver ASIC and On-Chip DLL-Based Time Interpolator for Indirect Time-of-Flight Applications

The applications requiring three-dimensional information have been growing rapidly, including facial recognition, gesture recognition, auto-focus for 2D camera, self-driving autonomous vehicles, etc. Indirect Time-of-Flight (iToF) technique has been adopted widely for 3D depth imaging due to its high spatial resolution and excellent depth accuracy performance. In this brief, a 200 MHz pulsed optical Illuminator for iToF applications is reported. A 940 nm GaAs-based triple-junction VCSEL array is adopted and an integrated pulsed VCSEL driver ASIC in 180 nm BCD process is realized. Optical pulses with peak power up to 14 W can be transmitted by the laser illuminator with a maximum driving current of 6.5 A by the driver ASIC. The modulation frequency can be up to 200 MHz. Automatic optical power control (AOPC) is realized for stable optical power output and laser eye safety protection. An on-chip DLL-based time interpolator is proposed to eliminate the mechanical moving components used during system calibration. The measured absolute accuracy of the time interpolator is +/−50 ps with a resolution of 800 ps and tuning range of 49.6 ns. This brief effectively extends the measurement distance of the iToF system. The calibration efficiency is also improved with the proposed method.

Performance of a resistive micro-well detector with capacitive-sharing strip anode readout

The present study is carried out to demonstrate a new type of high-performance readout structure with low readout channel count developed for large-area micro pattern gaseous detectors. The structure exploits capacitive coupling between a vertical stack of 5 μ m thick copper pad layers sandwiched between 50 μ m thick polyimide foils to simultaneously transfer and spread the avalanche charges from the gaseous detector’s amplification structure to several strips or pads in the anode readout plane. The unique feature of the lateral spread of the avalanche charge size on several readout strips or pads is possible owing to well-defined configuration and sizes of the pads in layers of the vertical stack. This is known as a “capacitive-sharing” readout structure and this opens the door for high spatial resolution performance with low readout channel counts for large-area Micro Pattern Gaseous Detectors. Capacitive-sharing readout structures are fabricated using standard printed circuits boards manufacturing process. The concept is highly versatile as it can easily be implemented in any type of Micro Pattern Gaseous Detector’s amplification structure (Gas Electron Multipliers, micro-mesh gaseous structures, resistive micro-well detectors) and with a wide range of readout patterns (pads, strips, zigzags etc.). The technology also has a high degree of flexibility in terms of readout segmentation (pads or strip) pitch, with minimum impact on spatial resolution performances. The present study provides a detailed description of the capacitive-sharing readout concept and discusses a small resistive micro-well detectors prototype assembled with a two-dimensional capacitive-sharing strip readout structure as a proof of concept and with strip pitch of 800 μ m in both X and Y direction. The prototype was characterized in electron beam in the Hall D Beam Test setup at Jefferson Lab and a spatial resolution of (60 ± 1) μ m was achieved for both X and Y strips with an efficiency of (98.0 ± 0.9) % at the plateau and a signal arrival time jitter between neighboring strips less (6.00 ± 0.04) ns. Finally, we explore new ideas to expand the concept of capacitive-sharing readout structures to large particle detectors for future large scale particle physics experiments.

Coherent Multi-Dwell Processing of Un-Synchronized Dwells for High Velocity Estimation and Super-Resolution in Radar

This paper describes a coherent multi-dwell processing (CMDP) method for high velocity estimation and super-resolution in search and track, while search (TWS) radar modes use an un-conventional signal processing algorithm that exploits multi-dwell transmissions. The existence of the multi-dwell waveform is necessary for visibility needs by un-folding the target’s velocity and range ambiguity and is proposed to be utilized for high velocity estimation and super-resolution. In this paper, the proposed scheme is shown to result in improved velocity estimation and doppler resolution performance for un-ambiguous targets in comparison to classical radar processing. The processing concept uses the same transmitted waveform (WF) and time duration without the need to increase the time on target (TOT) through sophisticated coherent concatenation of the received dwells with velocity compensation between the dwells. The phase compensation in receive mode is implemented for each target according to its characteristics, which means that target velocities are estimated in each dwell separately. The notable result of the CMDP is the linear doppler resolution improvement obtained with the given search resources and without knowing the target characteristics in advance or the dwell delay time. Other possible benefits of this process are the ability to achieve larger detection ranges and high-angle measurement precisions in search mode due to the higher signal-to-noise ratio (SNR) of the extended dwell and the ability to track more targets due to efficient time and resource management. An outstanding opportunity to exploit the CMDP is by combining missions in phased array (PA) radars, meeting the multi-objective needs of both high spatial scan rates for illuminating the target and high doppler estimation and resolution performance.

Motion Error Estimation and Compensation of Airborne Array Flexible SAR Based on Multi-Channel Interferometric Phase

Airborne array synthetic aperture radar (SAR) has made a significant breakthrough in the three-dimensional resolution of traditional SAR. In the airborne array SAR 3D imaging technology, the baseline length is the main factor restricting the resolution. Airborne array flexible SAR can increase the baseline length to improve the resolution and interference performance by mounting antennae on the wing. The existing research lacks results obtained using flexible actual data processing and specific motion compensation methods. Thus, this paper proposes a motion error estimation and compensation method for an airborne array flexible SAR based on a multi-channel interferometric phase. Firstly, a flexible channel motion compensation model is established based on the multi-channel interference phase of airborne array flexible SAR. Then, based on the rigid multi-channel data, combined with the ground control points, the least square method, and the global optimal search algorithm, the accurate rigid baseline length and the central incidence angle are obtained. Finally, according to the multi-channel interference phase inversion of the flexible motion error and combined with the motion compensation model, the flexible data are compensated in the time domain. The actual results indicate that, compared with traditional motion compensation methods, our method can obtain accurate flexible compensation data. This study improves the interference performance of multi-channel data of airborne array flexible SAR and lays a solid foundation for the high-precision 3D reconstruction of airborne array flexible SAR.

Investigation and Analysis of Thermoelectrically Cooled CZT Performance

Cadmium zinc telluride (CZT) spectrometers have been considered for objectives and missions with variable ambient temperatures. Spectrometer-grade crystals of various sizes have been studied under conditions as low as −40°C for 2 × 2 × 2 and 5 × 5 × 2-mm3 crystals, and −10°C for 5 × 5 × 5-mm3 crystals for resolution improvement spanning 5.9-, 59.5-, and 122-keV photo peak energies. It is unclear from previously published data if cooling the spectrometer-grade crystals beyond −10°C results in increased resolution improvement or if the effect occurs with higher-energy photo peaks and trends among multiple crystals from the same manufacturer. Therefore, we acquired two CZT crystals from Kromek and cooled them in an insulated box to −25°C. Our measurements were performed every 5°C, and tested with 241Am or 241Am/152Eu mixed sources. The 241Am peaks were compared for both crystals, and the higher-energy resolution changes were explored using the mixed source. Overall, at 59.5 keV, both crystals yielded 3% to 4% resolution improvement for the cooling cycle and 6% improvement during the warming cycle. Resolution performance varied between the two tested crystals, and each had a different temperature where we observed optimum resolution. The 121.8-keV peak resolution improved by 1.2% for the cooling cycle and 3.6% for the warming cycle. There were no discernable resolution increases or changes for the two higher-energy peaks, 224.7 and 334.3 keV, respectively. Slight cooling of the CZT crystals can increase resolution performance by 4% in the lower-energy region.

Using simulation to quantify the performance of automotive perception systems

The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance or to test its performance under conditions that are difficult to measure (e.g., nighttime for automotive perception systems). We describe the image system simulation software tools that we use to evaluate the performance of image systems for object (automobile) detection. We describe experiments with 13 different cameras with a variety of optics and pixel sizes. To measure the impact of camera spatial resolution, we designed a collection of driving scenes that had cars at many different distances. We quantified system performance by measuring average precision and we report a trend relating system resolution and object detection performance. We also quantified the large performance degradation under nighttime conditions, compared to daytime, for all cameras and a COCO pre-trained network.

Optimizing NV magnetometry for Magnetoneurography and Magnetomyography applications.

Magnetometers based on color centers in diamond are setting new frontiers for sensing capabilities due to their combined extraordinary performances in sensitivity, bandwidth, dynamic range, and spatial resolution, with stable operability in a wide range of conditions ranging from room to low temperatures. This has allowed for its wide range of applications, from biology and chemical studies to industrial applications. Among the many, sensing of bio-magnetic fields from muscular and neurophysiology has been one of the most attractive applications for NV magnetometry due to its compact and proximal sensing capability. Although SQUID magnetometers and optically pumped magnetometers (OPM) have made huge progress in Magnetomyography (MMG) and Magnetoneurography (MNG), exploring the same with NV magnetometry is scant at best. Given the room temperature operability and gradiometric applications of the NV magnetometer, it could be highly sensitive in the -range even without magnetic shielding, bringing it close to industrial applications. The presented work here elaborates on the performance metrics of these magnetometers to the state-of-the-art techniques by analyzing the sensitivity, dynamic range, and bandwidth, and discusses the potential benefits of using NV magnetometers for MMG and MNG applications.

DiffuserSpec: spectroscopy with Scotch tape.

Computational spectroscopy breaks the inherent one-to-one spatial-to-spectral pixel mapping of traditional spectrometers by multiplexing spectral data over a given sensor region. Most computational spectrometers require components that are complex to design, fabricate, or both. DiffuserSpec is a simple computational spectrometer that uses the inherent spectral dispersion of commercially available diffusers to generate speckle patterns that are unique to each wavelength. Using Scotch tape as a diffuser, we demonstrate narrowband and broadband spectral reconstructions with 2-nm spectral resolution over an 85-nm bandwidth in the near-infrared, limited only by the bandwidth of the calibration dataset. We also investigate the effect of spatial sub-sampling of the 2D speckle pattern on resolution performance.

Type-2 Neutrosophic Set and Their Applications in Medical Databases Deadlock Resolution

Electronic patient data gives many advantages, but also new difficulties. Deadlocks may delay procedures like acquiring patient information. Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties. Soft computing-based solutions have been developed to solve this challenge. In a single framework, ambiguous, vague, incomplete, and inconsistent transaction attribute information has received minimal attention. The work presented in this paper employed type-2 neutrosophic logic, an extension of type-1 neutrosophic logic, to handle uncertainty in real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among transactions’ features that include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity. Here, the footprint of uncertainty (FOU) for truth, indeterminacy, and falsity represents the level of uncertainty that exists in the value of a grade of membership. We employed a distributed real-time transaction processing simulator (DRTTPS) to conduct the simulations and conducted experiments using the benchmark Pima Indians diabetes dataset (PIDD). As the results showed, there is an increase in detection rate and a large drop in rollback rate when this new strategy is used. The performance of Type-2 neutrosophic-based resolution is better than the Type-1 neutrosophic-based approach on the execution ratio scale. The improvement rate has reached 10% to 20%, depending on the number of arrived transactions.

The 3-D Global Prestack Seismic Inversion in the Time–Frequency Mixed Domain

Traditional seismic inversion has the problems of low lateral resolution and poor anti-noise performance. To address these challenges, the advantages of inversion methods in time and frequency domains are combined; the Lp norm that can preserve more sparsity information is introduced into seismic inversion. Meanwhile, multi-trace simultaneous inversion has become the mainstream of inversion methods, which can effectively improve the quality of pre-stack seismic inversion. However, traditional multi-trace simultaneous inversion methods have high computational costs and are difficult to obtain the 3D result. Ensuring spatial continuity of inversion results is a challenging task. To address this issue, a 3D global pre-stack inversion method in the time-frequency mixed domain is proposed. The proposed method adds a frequency domain fidelity constraint the objective function of traditional time domain inversion to improve the resolution of the inversion results. To improve the computational efficiency of multi-trace simultaneous inversion, the anisotropic total variation regularization method under Lp norm constraint is employed. In addition, the 2D Sylvester equation is extended to 3D space to enable 3D global inversion. The method not only improves computational efficiency, but also ensures spatial continuity of the results. The stability and feasibility of the proposed method have been demonstrated through its application on both the 3D overthrust model and field pre-stack seismic data.

Three-Dimensional Ray-Based Tomographic Approach for Contactless GPR Imaging

This paper proposes a three-dimensional imaging approach for contactless ground penetrating radar surveys. The imaging problem is formulated in the linear inverse scattering context and solved by using the Singular Values Decomposition tool. A ray-based model accounting for the electromagnetic signal propagation into an inhomogeneous medium is developed to accurately evaluate the kernel of the integral equation to be inverted. Under the proposed model, an analysis of the spatial resolution performance is carried out as a function of the geometrical and electromagnetic parameters of the scenario. To this end, theoretical concepts based on diffraction tomography and the Singular Value Decomposition of the scattering operator are exploited. Reconstruction results based on full-wave simulated data assess the feasibility of the imaging approach.

Simulation study of a brain PET scanner using TOF-DOI detectors equipped with first interaction position detection

Objective. The aim of this study is to evaluate the performance characteristics of a brain positron emission tomography (PET) scanner composed of four-layer independent read-out time-of-flight depth-of-interaction (TOF-DOI) detectors capable of first interaction position (FIP) detection, using Geant4 application for tomographic emission(GATE). This includes the spatial resolution, sensitivity, count rate capability, and reconstructed image quality. Approach. The proposed TOF-DOI PET detector comprises four layers of a 50 × 50 cerium-doped lutetium–yttrium oxyorthosilicate (LYSO:Ce) scintillator array with 1 mm pitch size, coupled to a 16 × 16 multi-pixel photon counter array with 3.0 mm × 3.0 mm photosensitive segments. Along the direction distant from the center field-of-view (FOV), the scintillator thickness of the four layers is 2.5, 3, 4, and 6 mm. The four layers were simulated with a 150 ps coincidence time resolution and the independent readout make the FIP detection capable. The spatial resolution and imaging performance were compared among the true-FIP, winner-takes-all (WTA) and front-layer FIP (FL-FIP) methods (FL-FIP selects the interaction position located on the front-most interaction layer in all the interaction layers). The National Electrical Manufacturers Association NU 2-2018 procedure was referred and modified to evaluate the performance of proposed scanner. Main results. In detector evaluation, the intrinsic spatial resolutions were 0.52 and 0.76 mm full width at half-maximum (FWHM) at 0° and 30° incident γ-rays in the first layer pair, respectively. The reconstructed spatial resolution by the filter backprojection (FBP) achieved sub-millimeter FWHM on average over the whole FOV. The maximum true count rate was 207.6 kcps at 15 kBq ml−1 and the noise equivalent count rate (NECR_2R) was 54.7 kcps at 6.0 kBq ml−1. Total sensitivity was 45.2 cps kBq−1 and 48.4 cps kBq−1 at the center and 10 cm off-center FOV, respectively. The TOF and DOI reconstructions significantly improved the image quality in the phantom studies. Moreover, the FL-FIP outperformed the conventional WTA method in terms of the spatial resolution and image quality. Significance. The proposed brain PET scanner could achieve sub-millimeter spatial resolution and high image quality with TOF and DOI reconstruction, which is meaningful to the clinical oncology research. Meanwhile, the comparison among the three positioning methods indicated that the FL-FIP decreased the image degradation caused by Compton scatter more than WTA.

Porous Organic Cage-Embedded C10-Modified Silica as HPLC Stationary Phase and Its Multiple Separation Functions.

Reduced imine cage (RCC3) was covalently bonded to the surface of silica spheres, and then the secondary amine group of the molecular cage was embedded in non-polar C10 for modification to prepare a novel RCC3-C10@silica HPLC stationary phase with multiple separation functions. Through infrared spectroscopy, thermogravimetric analysis and nitrogen adsorption-desorption characterization, it was confirmed that RCC3-C10 was successfully bonded to the surface of silica spheres. The resolution of RCC3-C10@silica in reversed-phase separation mode is as high as 2.95, 3.73, 3.27 and 4.09 for p-phenethyl alcohol, 1-phenyl-2-propanol, p-methylphenethyl alcohol and 1-phenyl-1-propanol, indicating that the stationary phase has excellent chiral resolution performance. In reversed-phase and hydrophilic separation modes, RCC3-C10@silica realized the separation and analysis of a total of 70 compounds in 8 classes of Tanaka mixtures, alkylbenzene rings, polyphenyl rings, phenols, anilines, sulfonamides, nucleosides and flavonoids, and the analysis of a variety of chiral and achiral complex mixtures have been completed at the same time. Compared with the traditional C18 commercial column, RCC3-C10@silica exhibits better chromatographic separation selectivity, aromatic selectivity and polar selectivity. The multifunctional separation mechanism exhibited by the stationary phase originates from various synergistic effects such as hydrophobic interaction, π-π interaction, hydrogen bonding and steric interaction provided by RCC3 and C10 groups. This work provides flexible selectivity and application prospects for novel multi-separation functional chromatographic columns.