Plant-mediated fabrication and surface enhanced raman property of flower-like Au@Pd nanoparticles

The flower-like nanostructures of an Au core and Pd petals with the average size of 47.8 nm were fabricated through the successive reduction of HAuCl4 and Na2PdCl4 at room temperature. During the synthesis, Cacumen Platycladi leaf extract served as weak reductant and capping agent. Characterization techniques such as Energy-dispersive X-ray spectroscopy, UV-Vis spectroscopy, and X-ray diffraction characterizations were employed to confirm that the as-synthesized nanoparticles have the structure of core-shell. The obtained core-shell nanoflowers exhibited good surface enhanced Raman spectroscopic activity with Rhodamine 6G. ? 2014 by the authors

Biogenic flower-shaped Au-Pd nanoparticles: Synthesis, SERS detection and catalysis towards benzyl alcohol oxidation

～40 nm flower-shaped Au-Pd bimetallic nanoparticles were prepared in a facile and eco-friendly way based on the simultaneous bioreduction of HAuCl 4 and Na2PdCl4 with ascorbic acid and Cacumen Platycladi leaf extract at room temperature. Characterization techniques, such as transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction, were employed to confirm that the as-synthesized nanoparticles were alloys. The obtained flower-shaped Au-Pd alloy nanoparticles exhibited an excellent surface enhanced Raman spectroscopic activity with rhodamine 6G and efficient catalytic ability for the oxidation of benzyl alcohol to benzaldehyde. ? 2014 The Royal Society of Chemistry

Pain-causing stinging nettle toxins target TMEM233 to modulate NaV1.7 function

Voltage-gated sodium (NaV) channels are critical regulators of neuronal excitability and are targeted by many toxins that directly interact with the pore-forming α subunit, typically via extracellular loops of the voltage-sensing domains, or residues forming part of the pore domain. Excelsatoxin A (ExTxA), a pain-causing knottin peptide from the Australian stinging tree Dendrocnide excelsa, is the first reported plant-derived NaV channel modulating peptide toxin. Here we show that TMEM233, a member of the dispanin family of transmembrane proteins expressed in sensory neurons, is essential for pharmacological activity of ExTxA at NaV channels, and that co-expression of TMEM233 modulates the gating properties of NaV1.7. These findings identify TMEM233 as a previously unknown NaV1.7-interacting protein, position TMEM233 and the dispanins as accessory proteins that are indispensable for toxin-mediated effects on NaV channel gating, and provide important insights into the function of NaV channels in sensory neurons

Palladium-catalyzed approaches to indenes

In this thesis several palladium-catalyzed carboannulation methods have been developed for a variety of indene derivatives. Chapter 1 describes the synthesis of indenes by the palladium-catalyzed carboannulation of internal alkynes by functionalized aryl halides. The annulation proceeds under relatively mild reaction conditions and gives good yields of indenes. This annulation process also exhibits excellent regioselectivity and is particularly suited for the synthesis of hindered 2-substituted indenes. Chapter 2 deals with a palladium/copper-catalyzed coupling of terminal alkynes and aryl halides, followed by a copper-catalyzed intramolecular cyclization. This two-step annulation procedure has proven to be quite general for the synthesis of indenes from terminal alkynes bearing a variety of substituents. Chapter 3 presents a new method for the synthesis of indenes by the palladium-catalyzed arylation of arylalkynes bearing a carbon nucleophile. This procedure, which involves cyclization and arylation in a single step, provides a convenient means of synthesizing indenes in high yields. This reaction also tolerates considerable functionality, and is particularly suited for the synthesis of 3,4-diaryl substituted indenes from electron-deficient aryl halides.</p

Interaction between graphene-coated nanowires revisited with transformation optics

The interaction between graphene-coated nanostructures provides interesting optical properties not found in isolated graphene plasmonic structures. However, full-analytical solutions, which can provide deep physical insights underlying the hybrid graphene plasmonic systems, are difficult to achieve. In this Letter, we deploy the theory of transformation optics to study the plasmonic interactions between two dielectric-core-graphene-shell nanowires. The scattering and absorption spectra as well as the field distributions are derived analytically. We find that the interaction between two graphene-coated nanowires results in polarization-independent multi-frequency Fano dips, which show a broadband red shift of bonding modes and a blue shift of anti-bonding modes when the nanowires approach each other. The analytical tool presented here offers a rigorous study of graphene plasmonic compound and can be extended to treat more complicated cases.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Accepted versio

Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM

Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices