Interest of molecular functionalization for electrochemical storage

Despite great interests in electrochemical energy storage systems for numerous applications, considerable challenges remain to be overcome. Among the various approaches to improving the stability, safety, performance, and cost of these systems, molecular functionalization has recently been proved an attractive method that allows the tuning of material surface reactivity while retaining the properties of the bulk material. For this purpose, the reduction of aryldiazonium salt, which is a versatile method, is considered suitable; it forms robust covalent bonds with the material surface, however, with the formation of multilayer structures and sp³ defects (for carbon substrate) that can be detrimental to the electronic conductivity. Alternatively, non-covalent molecular functionalization based on π–π interactions using aromatic ring units has been proposed. In this review, the various advances in molecular functionalization concerning the current limitations in lithium-ion batteries and electrochemical capacitors are discussed. According to the targeted applications and required properties, both covalent and non-covalent functionalization methods have proved to be very efficient and versatile. Fundamental aspects to achieve a better understanding of the functionalization reactions as well as molecular layer properties and their effects on the electrochemical performance are also discussed. Finally, perspectives are proposed for future implementation of molecular functionalization in the field of electrochemical storage.

Open image in new window

     

Engineered Multifunctional Albumin‐Decorated Porous Silicon Nanoparticles for FcRn Translocation of Insulin

The last decade has seen remarkable advances in the development of drug delivery systems as alternative to parenteral injection‐based delivery of insulin. Neonatal Fc receptor (FcRn)‐mediated transcytosis has been recently proposed as a strategy to increase the transport of drugs across the intestinal epithelium. FcRn‐targeted nanoparticles (NPs) could hijack the FcRn transcytotic pathway and cross the epithelial cell layer. In this study, a novel nanoparticulate system for insulin delivery based on porous silicon NPs is proposed. After surface conjugation with albumin and loading with insulin, the NPs are encapsulated into a pH‐responsive polymeric particle by nanoprecipitation. The developed NP formulation shows controlled size and homogeneous size distribution. Transmission electron microscopy (TEM) images show successful encapsulation of the NPs into pH‐sensitive polymeric particles. No insulin release is detected at acidic conditions, but a controlled release profile is observed at intestinal pH. Toxicity studies show high compatibility of the NPs with intestinal cells. In vitro insulin permeation across the intestinal epithelium shows approximately fivefold increase when insulin is loaded into FcRn‐targeted NPs. Overall, these FcRn‐targeted NPs offer a toolbox in the development of targeted therapies for oral delivery of insulin.     

Recent advances in controlled modification of the size and morphology of metal-organic frameworks

Advances in metal-organic frameworks (MOFs) resulted in significant contributions to diverse applications such as carbon capture, gas storage, heat transformation and separation along with emerging applications toward catalysis, medical imaging, drug delivery, and sensing. The unique in situ and ex situ structural features of MOFs can be tailored by conceptual selection of the organic (e.g., ligand) and inorganic (e.g., metal) components. Here, we provide a comprehensive review on the synthesis and characterization of MOFs, particularly with respect to controlling their size and morphology. A better understanding of the specific size and morphological parameters of MOFs will help initiate a new era for their real-world applications. Most importantly, this assessment will help develop novel synthesis methods for MOFs and their hybrid/porous materials counterparts with considerably improved properties in targeted applications.

Open image in new window

     

BallotMaps: detecting name bias in alphabetically ordered ballot papers

The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.     

Shortcut fusion rules for the derivation of circular and higher-order programs

Functional programs often combine separate parts using intermediate data structures for communicating results. Programs so defined are modular, easier to understand and maintain, but suffer from inefficiencies due to the generation of those gluing data structures. To eliminate such redundant data structures, some program transformation techniques have been proposed. One such technique is shortcut fusion, and has been studied in the context of both pure and monadic functional programs. In this paper, we study several shortcut fusion extensions, so that, alternatively, circular or higher-order programs are derived. These extensions are also provided for effect-free programs and monadic ones. Our work results in a set of generic calculation rules, that are widely applicable, and whose correctness is formally established.     

Spoken emotion recognition through optimum-path forest classification using glottal features

A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks – multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.