Facile Fabrication of Polymer Electrolytes with Branched Structure via Deep Eutectic Electrolyte-Enabled In Situ Polymerizations.

The demand for higher energy density in energy storage devices drives further research on lithium metal batteries (LMBs) because of the high theoretical capacity and low voltage of lithium metal anode. Polymer electrolytes (PEs) exhibit obvious advantages in combating volatilization and leakage compared with liquid electrolytes, which improves the safety of LMBs. However, it is still difficult to construct PEs with a stable electrolyte-electrode interface for high-performance and long-term life LMBs. Herein, the gel polymer electrolyte (GPE-SL) containing deep eutectic electrolyte (DEE) and branchlike polymer skeleton are designed and prepared by the DEE-induced in situ cationic and radical polymerizations. The DEE provides a smooth Li+ migration pathway to ensure the electrochemical properties, and the multibrominated polymer matrix formed in situ enables a LiBr-rich solid electrolyte interphase (SEI) layer on lithium metal anode and prolongs the life span of LMBs. Hence, the Li|GPE-SL|LiFePO4 battery displays an excellent cycling stability with 84% capacity retention after 1200 cycles at 1C. This simple deep eutectic electrolyte-induced polymerization method provides a promising direction for high-performance LMBs with improved anode-electrolyte compatibility through the construction of a stable SEI layer in situ.

One-Step In Situ Polymerization: A Facile Design Strategy for Block Copolymer Electrolytes.

To optimize the rapid transport of lithium ions (Li+ ) inside lithium metal batteries (LMBs), block copolymer electrolytes (BCPEs) have been fabricated in situ in LMBs via a one-step method combining reversible addition-fragmentation chain transfer (RAFT) polymerization and carboxylic acid-catalyzed ring-opening polymerization (ROP). The BCPEs balanced the Li+ coordination characteristics of the polyether- and polyester-based electrolytes to achieve a rapid Li+ migration in the SPEs. The carboxylic acid played a dual role since it both catalyzed the ROP and stabilized the interface. Furthermore, the in situ assembly of LMBs did effectively enable an efficient intercalation/de-intercalation of Li+ at the electrode/electrolyte interface. The in situ assembled Li/BCPE4/LFP exhibited high-capacity retention of 92 % after 400 cycles at 1 C. The one-step in situ fabrication of BCPEs provides a new direction for the design of polymer electrolytes.

Bovine α-lactalbumin-derived peptides attenuate TNF-α-induced insulin resistance and inflammation in 3T3-L1 adipocytes through inhibiting JNK and NF-κB signaling.

Bioactive peptides in bovine α-lactalbumin were isolated and identified, and the effects and mechanisms of peptide KILDK on insulin resistance in 3T3-L1 adipocytes were investigated. Mature 3T3-L1 adipocytes were stimulated with TNF-α to induce insulin resistance. Bovine α-lactalbumin hydrolysates (α-LAH) were subjected to stimulated gastrointestinal digestion and Caco-2 absorption, and GD-α-LAH and CA-α-LAH were obtained. Our results demonstrated that α-LAH, GD-α-LAH, and CA-α-LAH increased glucose uptake, enhanced Akt phosphorylation (Ser473), and decreased IRS-1 phosphorylation (Ser307) in insulin resistant 3T3-L1 adipocytes. Gel filtration chromatography and liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI MS/MS) were used to separate and identify bioactive peptides. The identified peptide KILDK attenuated insulin resistance in 3T3-L1 adipocytes, which was attributed to the suppression of JNK phosphorylation (Thr183/Tyr185). Moreover, KILDK downregulated pro-inflammatory genes through blocking NF-κB signaling. Our findings suggested that bovine α-LAH might be a potential ingredient against insulin resistance.

Visible-light-induced triple catalysis for a ring-opening cyanation of cyclopropyl ketones.

An unprecedented triple catalytic, general ring-opening cyanation reaction of cyclopropyl ketones for the construction of γ-cyanoketones is described. The key is to merge photoredox catalysis with Lewis acid catalysis and copper catalysis to enable the selective cleavage of the carbon-carbon bonds and the selective coupling of the generated radical and cyanide anion.

Stabilizing Liquid Electrolytes in a Porous PVDF Matrix Incorporated with Star Polymers with Linear PEG Arms and CycloPEG Cores.

Porous membranes fabricated from poly(vinylidene fluoride) (PVDF) and a star polymer with linear poly(ethylene glycol) (PEG) arms and cycloPEG cores were fabricated via the phase-separation method. The porous gel polymer electrolytes (PGPEs) were obtained by immersing the porous membranes in the electrolyte solution. When the additive amount of star polymer was up to 20 wt %, the prepared membrane had the largest porosity and the pores were uniformly distributed in the membrane. The star polymer can not only decrease the crystallization of PVDF and enhance the absorption of liquid electrolyte but also offer ion conduction channels (cycloPEG cores). Therefore, the PGPE with 20 wt % star polymers exhibited competitive ionic conductivities of 1.27 mS cm-1 at 30 °C and 2.89 mS cm-1 at 80 °C. To stabilize the liquid electrolyte in the holes of porous membranes, a gelator was introduced in the liquid electrolyte to form gelled porous gel polymer electrolytes (GPGPEs), and the leakage of liquid electrolytes was thus remarkably reduced. The ionic conductivity of GPGPEs with 20 wt % star polymer and 1.5 wt % gelator was importantly improved at high temperatures (6.02 mS cm-1 at 80 °C). We systematically investigated the electrochemical performances of PGPEs without star polymer, PGPEs with star polymer, and GPGPEs with star polymer. The incorporation of star polymers with linear PEG arms and cycloPEG cores into the PGPEs and GPGPEs significantly improved the electrochemical performances of the lithium metal/LiFePO4 cell assembled with the PGPEs or GPGPEs.

A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier.

Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.