Characterization of Polymers by Direct Pyrolysis/Chemical Ionization Mass Spectrometry

The characterization of polymers by pyrolysis directly in the ion source of a double focusing magnetic sector mass spectrometer, operating in the chemical ionization mode, is described. Pyrolysis is achieved by two different probe techniques. A low temperature, slow heating rate direct insertion probe (DIP) is used at 400°C, and a specifically constructed high temperature, fast heating rate, high temperature pyrolysis (HTP) probe is used at 1000°C. This probe is capable of achieving pyrolysis temperatures of 1200°C at controlled heating rates up to 20,000°C/s. The mass spectrometric analysis of the pyrolysis products was achieved under chemical ionization (CI) conditions utilizing methane, isobutane, and ammonia as reagent gases. Under CI conditions the molecular ions formed in the mass spectrometer show little tendency to fragment. The CI mass pyrograms are very simple, with each peak in the spectra ascribable to a particular component in the pyrolysis product mixture. The results of the two probe pyrolysis techniques are compared and the utility of each technique for the characterization of polymers is demonstrated using the vinyl polymers polymethyl methacrylate, polyvinyl chloride, and polystyrene.     

Selective recognition and electrochemical detection of p-nitrophenol based on a macroporous imprinted polymer containing gold nanoparticles

We have combined the molecular imprinting and the layer-by-layer assembly techniques to obtain molecularly imprint polymers (MIPs) for the electrochemical determination of p-nitrophenol (p-NPh). Silica microspheres functionalized with thiol groups and gold nanoparticles (Au-NPs) were assembled on a gold electrode surface layer by layer. The electrode was then immersed into a solution of pyrrole and p-NPh (the template), and electropolymerization led to the creation of a polymer-modified surface. After the removal of the silica spheres and the template, electrochemical impedance spectroscopy and differential pulse voltammetry (DPV) were employed to characterize the surface. The results demonstrated the successful fabrication of macroporous MIPs embedded with Au-NPs on the gold electrode. The effects of monomer concentration and scan rate on the performance of the electrode were optimized. Excellent recognition capacity is found for p-NPh over chemically similar species. The DPV peak current is linearly related to concentration of p-NPh in the 0.1 μM to 1.4 mM range, with a 0.1 μM limit of detection (at S/N?=?3).

Entanglement of n-heptane and iso-butanol chemistries in flames fueled by their mixtures

This paper concerns itself with the entanglement of the high-temperature oxidation chemistry of n-heptane and iso-butanol in flames fueled by their mixtures. While in many cases the chemistries of the individual fuel components do not interact in mixture flames, in this work, we revealed interactions between the individual species pools originating from n-heptane and iso-butanol oxidation. In a coordinated experimental and modeling effort, chemical structures of three low-pressure premixed flames fueled by different blends of n-heptane and iso-butanol were determined using flame-sampling molecular-beam mass spectrometry with synchrotron-based single-photon ionization and chemical kinetic modeling. The chemical kinetic model, which is based on the reaction set that was used previously [Braun-Unkhoff et al., Proc. Combust. Inst., 2017, 36, 1311–1319], was now extended by an n-heptane sub-mechanism. The overall good performance of the model allows for an extraction of chemically relevant information that highlights the entanglement between the individual fuel-specific species pools. For example, it was shown that methyl radicals, in part from iso-butanol oxidation (i.e., from the decomposition of α-iso-butanol radicals) can participate in n-heptane consumption processes through H-abstraction reactions. Further interactions are related to the formation of the methylallyl radical and aromatics formation. The relevance of such interactions is also discussed regarding the formation of oxygenated byproducts.     

Fluorescein-bridged Perylene Bisimide Dimer for Use as Liquid Crystal: Studies on Mesomorphic and Fluorescence Properties

Journal of Fluorescence - A novel fluorescein-bridged perylene bisimide (PBI) dimer for liquid crystal (LC) with geometrically symmetric structure was developed. The mesomorphic results indicated...     

O-GlcNAcylation mapping of single living cells by in situ quantitative SERS imaging

O-GlcNAcylation is involved in many biological processes including cancerization. Nevertheless, its in situ quantification in single living cells is still a bottleneck. Here we develop a quantitative SERS imaging strategy for mapping the O-GlcNAcylation distribution of single living cells. O-GlcNAcylated compounds (OGCs) can be quantified through their in situ azide labeling and then a click reaction competing with azide and Raman reporter labeled 15 nm-gold nanoparticles (AuNPs) for linking to dibenzocyclooctyne labeled 40 nm-AuNPs to produce OGC-negatively correlated SERS signals. The calibration curve obtained in vitro can be conveniently used for detecting OGCs in different areas of single living cells due to the negligible effect of cell medium on the click linkage and Raman signal. This method has been successfully applied in mapping O-GlcNAcylation distribution in different cell lines and monitoring O-GlcNAcylation variation during cell cycling, which demonstrate its great practicability and expansibility in glycosylation related analysis.

A quantitative SERS imaging strategy is developed for O-GlcNAcylation mapping of single living cells through a competitive click reaction.     

Unraveling hydridic-to-protonic dihydrogen bond predominance in monohydrated dodecaborate clusters

Hydridic-to-protonic dihydrogen bonds (DHBs) are involved in comprehensive structural and energetic evolution, and significantly affect reactivity and selectivity in solution and solid states. Grand challenges exist in understanding DHBs’ bonding nature and strength, and how to harness DHBs. Herein we launched a combined photoelectron spectroscopy and multiscale theoretical investigation using monohydrated closo-dodecaborate clusters B₁₂X₁₂²⁻·H₂O (X = H, F, I) to address such challenges. For the first time, a consistent and unambiguous picture is unraveled demonstrating that B–H⋯H–O DHBs are superior to the conventional B–X⋯H–O HBs, being 1.15 and 4.61 kcal mol⁻¹ stronger than those with X = F and I, respectively. Energy decomposition analyses reveal that induction and dispersion terms make pronounced contributions resulting in a stronger B–H⋯H–O DHB. These findings call out more attention to the prominent roles of DHBs in water environments and pave the way for efficient and eco-friendly catalytic dihydrogen production based on optimized hydridic-to-protonic interactions.

A joint gas-phase ion spectroscopic and multiscale theoretical study reveals unequivocally the predominance of the hydridic-to-protonic dihydrogen bond over the prototypical strong hydrogen bond in monohydrated dodecaborate clusters.     

Research on Throughput-Guaranteed MAC Scheduling Policies in Wireless Networks

In wireless networks, MAC scheduling methods can be divided into two types according to the implementation model: centralized and distributed scheduling. By reasonably designing MAC scheduling policies, both centralized and distributed schedulers can ensure a reliable throughput capacity region, i.e., realizing throughput-guaranteed. However, it can be found that some existing throughput-guaranteed scheduling schemes cannot further ensure bounded end-to-end average delay, and the reason for this phenomenon has not been deeply analyzed. In practical communication networks, throughput and delay are equally important. Based on this idea, the existing MAC scheduling strategies are investigated systematically in this paper from two aspects of throughput and delay, and their performances are evaluated and compared through both theoretical analysis and simulation experiments. The work of this paper provides a theoretical basis for the improvement of MAC scheduling technology in the next-generation wireless networks.     

Robustness Evaluation of the Open Source Product Community Network Considering Different Influential Nodes

With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness are traditionally used to evaluate the importance of nodes. However, these two indexes are disabled to comprehensively evaluate the influential nodes in the community network. Furthermore, influential users have many followers. The effect of irrational following behavior on network robustness is also worth investigating. To solve these problems, we built a typical OSPC network using a complex network modeling method, analyzed its structural characteristics and proposed an improved method to identify influential nodes by integrating the network topology characteristics indexes. We then proposed a model containing a variety of relevant node loss strategies to simulate the changes in robustness of the OSPC network. The results showed that the proposed method can better distinguish the influential nodes in the network. Furthermore, the network’s robustness will be greatly damaged under the node loss strategies considering the influential node loss (i.e., structural hole node loss and opinion leader node loss), and the following effect can greatly change the network robustness. The results verified the feasibility and effectiveness of the proposed robustness analysis model and indexes.     

Design and Embedded Implementation of Secure Image Encryption Scheme Using DWT and 2D-LASM

In order to further improve the information effectiveness of digital image transmission, an image-encryption algorithm based on 2D-Logistic-adjusted-Sine map (2D-LASM) and Discrete Wavelet Transform (DWT) is proposed. First, a dynamic key with plaintext correlation is generated using Message-Digest Algorithm 5 (MD5), and 2D-LASM chaos is generated based on the key to obtain a chaotic pseudo-random sequence. Secondly, we perform DWT on the plaintext image to map the image from the time domain to the frequency domain and decompose the low-frequency (LF) coefficient and high-frequency (HF) coefficient. Then, the chaotic sequence is used to encrypt the LF coefficient with the structure of “confusion-permutation”. We perform the permutation operation on HF coefficient, and we reconstruct the image of the processed LF coefficient and HF coefficient to obtain the frequency-domain ciphertext image. Finally, the ciphertext is dynamically diffused using the chaotic sequence to obtain the final ciphertext. Theoretical analysis and simulation experiments show that the algorithm has a large key space and can effectively resist various attacks. Compared with the spatial-domain algorithms, this algorithm has great advantages in terms of computational complexity, security performance, and encryption efficiency. At the same time, it provides better concealment of the encrypted image while ensuring the encryption efficiency compared to existing frequency-domain methods. The successful implementation on the embedded device in the optical network environment verifies the experimental feasibility of this algorithm in the new network application.     

Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis

Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some “dark genes” that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.