Efficient light confinement with nanostructured optical microfiber tips

Nanostructured optical microfiber tips are proposed and experimentally demonstrated to efficiently confine light beyond the diffraction limit at high powers. Focused ion beam milling was used for the nanostructuring of gold-coated optical microfiber tips with both single-ramp and wedge geometries. Small apertures were formed by flat cutting or hole drilling and optical spot sizes of ~λ/10 with high transmission efficiency were achieved. Numerical simulations were carried out to optimize the device design with circularly polarized light. Enhanced transmission efficiencies (higher than 10-2) were recorded by optimizing the overall light throughput along the fiber tips. The tip thermal behavior was investigated by launching high powers into the device and recording the tip position in a scanning near-field optical microscopy set-up. This nanostructured optical microfiber tip has the potential for applications in optical recording, scanning near-field optical microscopy and lithography

The outcome and cost-effectiveness of nurse-led care in the community for people with rheumatoid arthritis:a non-randomised pragmatic study

To determine the outcome and cost-effectiveness of nurse-led care in the community for people with rheumatoid arthritis (RA)

Controlling the thermoelectric properties of organo-metallic coordination polymers through backbone geometry

Poly(nickel-benzene-1,2,4,5-tetrakis(thiolate)) (Ni-btt), an organometallic coordination polymer (OMCP) characterized by the coordination between benzene-1,2,4,5-tetrakis(thiolate) (btt) and Ni2+ ions, has been recognized as a promising p-type thermoelectric material. In this study, we employed a constitutional isomer based on benzene-1,2,3,4-tetrakis(thiolate) (ibtt) to generate the corresponding isomeric polymer, poly(nickel-benzene-1,2,3,4-tetrakis(thiolate)) (Ni-ibtt). Comparative analysis of Ni-ibtt and Ni-btt reveals several common infrared (IR) and Raman features attributed to their similar square-planar nickel–sulfur (Ni–S) coordination. Nevertheless, these two polymer isomers exhibit substantially different backbone geometries. Ni-btt possesses a linear backbone, whereas Ni-ibtt exhibits a more undulating, zig-zag-like structure. Consequently, Ni-ibtt demonstrates slightly higher solubility and an increased bandgap in comparison to Ni-btt. The most noteworthy dissimilarity, however, manifests in their thermoelectric properties. While Ni-btt exhibits p-type behavior, Ni-ibtt demonstrates n-type carrier characteristics. This intriguing divergence prompted further investigation into the influence of OMCP backbone geometry on the electronic structure and, particularly, the thermoelectric properties of these materials

Critical analysis of self-doping and water-soluble n-type organic semiconductors: structures and mechanisms

Self-doping organic semiconductors provide a promising route to avoid instabilities and morphological issues associated with molecular n-type dopants. Structural characterization of a naphthalenetetracarboxylic diimide (NDI) semiconductor covalently bound to an ammonium hydroxide group is presented. The dopant precursor was found to be the product of an unexpected base catalyzed hydrolysis, which was reversible. The reversible hydrolysis had profound consequences on the chemical composition, morphology, and electronic performance of the doped films. In addition, we investigated the degradation mechanism of the quaternary ammonium group and the subsequent doping of NDI. These findings reveal that the products of more than one chemical reaction during processing of films must be considered when utilizing this promising class of water-soluble semiconductors

Uncertainty quantification of medium-term heat storage from short-term geophysical experiments using Bayesian Evidential Learning

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non-favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre- and post-field data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements. Plain Language Summary : Geothermal energy can be extracted or stored in shallow aquifers through systems called aquifer thermal energy storage (ATES). In practice, the energy efficiency of those systems is often lower than expected because of the uncertainty related to the subsurface. To assess the uncertainty, a common method in the scientific community is to generate multiple models of the subsurface fitting the available data, a process called stochastic inversion. However this process is time consuming and difficult to apply in practice for real systems. In this contribution, we develop a novel approach to avoid the inversion process called Bayesian Evidential Learning. We are still using many models of the subsurface, but we do not try to fit the available data. Instead, we use the model to learn a direct relationship between the data and the response of interest to the user. For ATES systems, this response corresponds to the energy extracted from the system. It allows to predict the amount of energy extracted with a quantification of the uncertainty. This framework makes uncertainty assessment easier and faster, a prerequisite for robust risk analysis and decision making. We demonstrate the method in a feasibility study of ATES design

Errors in the measurement of voltage-activated ion channels in cell-attached patch-clamp recordings

Patch-clamp recording techniques have revolutionized understanding of the function and sub-cellular location of ion channels in excitable cells. The cell-attached patch-clamp configuration represents the method of choice to describe the endogenous properties of voltage-activated ion channels in the axonal, somatic and dendritic membrane of neurons, without disturbance of the intracellular milieu. Here, we directly examine the errors associated with cell-attached patch-clamp measurement of ensemble ion channel activity. We find for a number of classes of voltage-activated channels, recorded from the soma and dendrites of neurons in acute brain-slices and isolated cells, that the amplitude and kinetics of ensemble ion channel activity recorded in cell-attached patches is significantly distorted by transmembrane voltage changes generated by the flow of current through the activated ion channels. We outline simple error–correction procedures that allow a more accurate description of the density and properties of voltage-activated channels to be incorporated into computational models of neurons

Tuning the energetics and tailoring the optical properties of silver clusters confined in zeolites

The integration of metal atoms and clusters in well-defined dielectric cavities is a powerful strategy to impart new properties to them that depend on the size and geometry of the confined space as well as on metal-host electrostatic interactions. Here, we unravel the dependence of the electronic properties of metal clusters on space confinement by studying the ionization potential of silver clusters embedded in four different zeolite environments over a range of silver concentrations. Extensive characterization reveals a strong influence of silver loading and host environment on the cluster ionization potential, which is also correlated to the cluster's optical and structural properties. Through fine-tuning of the zeolite host environment, we demonstrate photoluminescence quantum yields approaching unity. This work extends our understanding of structure property relationships of small metal clusters and applies this understanding to develop highly photoluminescent materials with potential applications in optoelectronics and bioimaging