Optical nanofiber stretcher.

Piezoelectric stretching of optical fiber is a technique that enables the creation of optical delays of a few picoseconds; this is useful in a variety of applications in interferometry or optical cavities. Most commercial fiber stretchers involve lengths of fiber of a few tens of meters. Using a 120-mm-long optical micro-nanofiber, we can create a compact optical delay line that achieves tunable delays of up to 19 ps at telecommunication wavelengths. The high elasticity of silica and the micron-scale diameter allow this significant optical delay to be achieved with low tensile force while keeping the overall length short. We successfully report both static and dynamic operation of this novel, to the best of our knowledge, device. It could find application in interferometry and laser cavity stabilization, where short optical paths and strong resistance to the environment would be required.

Large evanescently-induced Brillouin scattering at the surrounding of a nanofibre.

Brillouin scattering has been widely exploited for advanced photonics functionalities such as microwave photonics, signal processing, sensing, lasing, and more recently in micro- and nano-photonic waveguides. Most of the works have focused on the opto-acoustic interaction driven from the core region of micro- and nano-waveguides. Here we observe, for the first time, an efficient Brillouin scattering generated by an evanescent field nearby a single-pass sub-wavelength waveguide embedded in a pressurised gas cell, with a maximum gain coefficient of 18.90 ± 0.17 m-1W-1. This gain is 11 times larger than the highest Brillouin gain obtained in a hollow-core fibre and 79 times larger than in a standard single-mode fibre. The realisation of strong free-space Brillouin scattering from a waveguide benefits from the flexibility of confined light while providing a direct access to the opto-acoustic interaction, as required in free-space optoacoustics such as Brillouin spectroscopy and microscopy. Therefore, our work creates an important bridge between Brillouin scattering in waveguides, Brillouin spectroscopy and microscopy, and opens new avenues in light-sound interactions, optomechanics, sensing, lasing and imaging.

Micronewton nanofiber force sensor using Brillouin scattering.

We present a new class of force sensor based on Brillouin scattering in an optical nanofiber. The sensor is a silica nanofiber of a few centimeters with a submicron transverse dimension. This extreme form factor enables one to measure forces ranging from 10 µN to 0.2N. The linearity of the sensor can be ensured using the multimode character of the Brillouin spectrum in optical nanofibers. We also demonstrated non-static operation and a competitive signal-to-noise ratio as compared to commercial force sensor resistor.

Microscopic imaging along tapered optical fibers by right-angle Rayleigh light scattering in linear and nonlinear regime.

The evolution of the light intensity along an optical waveguide is evaluated by analysing far-field right-angle Rayleigh light scattering. The method is based on point by point spectral mapping distributed along the optical waveguide with a micrometric spatial resolution given by a confocal microscope, a high spectral resolution given by a spectrometer, and a high signal-to-noise ratio given by a highly cooled detector. This non-destructive and non-invasive experimental method allows the observation of the general Rayleigh scattering profile of the optical waveguide in a nominal operation, i.e., whatever the power or the wavelength of the light source, and can be applied to micrometer-scale waveguides of several centimeters in length, for which the longitudinal characterization is challenging. Applied to a tapered optical fiber, called nanofiber, with submicrometer final diameter and several centimeters long, the method has proved its capacity to collect different optical characteristics such as optical losses, mode beatings, transition from core-cladding to cladding-air guidance for different modes, localization of punctual defects, leaking of high order modes no longer guided by the fiber. Furthermore, the experimental results are successfully compared to measurements provided by the state-of-the-art Optical Backscatter Reflectometer system, and to numerical simulations. Moreover, coupled to the spectral resolution of the spectrometer, the method have allowed the distributed measurements of the Raman cascading process along the nanofiber, for the first time to our knowledge. The experimental technique developed in this work is complementary to other characterization methods generally focused on the optical parameters of the waveguide input or output. This technique can also be extended to others waveguides whatever its geometry which represents a strong interest for deepen optical characterization of photonics waveguides, or for other optical regimes characterized by spectral evolution of the field propagating along the waveguide.

Demonstration of the evanescent Kerr effect in optical nanofibers.

Optical nanofibers have recently emerged as attractive nanophotonic platforms for many applications ranging from quantum technologies to nonlinear optics, due to both their tight optical confinement and their wide evanescent field. Herein we examine theoretically the optical Kerr effect induced by the evanescent field of a silica nanofiber surrounded by different nonlinear liquids such as water, ethanol and acetone and we further compare them with air cladding. Our results show that the evanescent Kerr effect significantly dominates the usual Kerr effect inside the silica core for sub-wavelength diameters below 560 nm, using acetone. We further report the observation of the evanescent Kerr effect through surrogate measurements of stimulated Raman-Kerr scattering (SRKS) in an acetone-immersed silica nanofiber. Our findings open the way towards potential applications of optical nanofibers to ultra-sensitive liquid sensing or to enhancing the nonlinear effects through the evanescent field.

Global QSAR models of skin sensitisers for regulatory purposes.

BACKGROUND: The new European Regulation on chemical safety, REACH, (Registration, Evaluation, Authorisation and Restriction of CHemical substances), is in the process of being implemented. Many chemicals used in industry require additional testing to comply with the REACH regulations. At the same time EU member states are attempting to reduce the number of animals used in experiments under the 3 Rs policy, (refining, reducing, and replacing the use of animals in laboratory procedures). Computational techniques such as QSAR have the potential to offer an alternative for generating REACH data. The FP6 project CAESAR was aimed at developing QSAR models for 5 key toxicological endpoints of which skin sensitisation was one. RESULTS: This paper reports the development of two global QSAR models using two different computational approaches, which contribute to the hybrid model freely available online. CONCLUSIONS: The QSAR models for assessing skin sensitisation have been developed and tested under stringent quality criteria to fulfil the principles laid down by the OECD. The final models, accessible from CAESAR website, offer a robust and reliable method of assessing skin sensitisation for regulatory use.

Comparing the information content of two large olfactory databases.

The expert's subjectivity in establishing an olfactory description can produce wide discrepancies in different databases listing the odor profile of identical compounds. A representative example is obtained by comparing the odorous compounds included in the "Perfumery Materials and Performance 2001" (PMP2001) database and in Arctander's books (1960 and 1969). To better assess this problem, classification models obtained by using the adaptive fuzzy partition method were established on subsets of these databases distributed into the same olfactory classes. The robustness and the prediction power of these models give a powerful criterion for evaluating the "quality" of their information content and for deciding which is the most trustable database.

Classification of a large anticancer data set by adaptive fuzzy partition.

An Adaptive Fuzzy Partition (AFP) algorithm, derived from Fuzzy Logic concepts, was used to classify an anticancer data set, including about 1300 compounds subdivided into eight mechanisms of action. AFP classification builds relationships between molecular descriptors and bio-activities by dynamically dividing the descriptor hyperspace into a set of fuzzy subspaces. These subspaces are described by simple linguistic rules, from which scores ranging between 0 and 1 can be derived. The latter values define, for each compound, the degrees of membership of the different mechanisms analyzed. A particular attention was devoted to develop structure-activity relations that have a real utility. Then, well-defined and widely accepted protocols were used to validate the models by defining their robustness and prediction ability. More particularly, after selecting the most relevant descriptors with help of a genetic algorithm, a training set of 640 compounds was isolated by a rational procedure based on Self-Organizing Maps. The related AFP model was then validated with help of a validation set and, above all, of cross-validation and Y-randomization procedures. Good validation scores of about 80% were obtained, underlining the robustness of the model. Moreover, the prediction ability was evaluated with 374 test compounds that had not been used to establish the model and 77% of them were predicted correctly.

Prediction of oral bioavailability by adaptive fuzzy partitioning.

An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity.

Database mining with adaptive fuzzy partition: application to the prediction of pesticide toxicity on rats.

A data set of 235 pesticide compounds, divided into three classes according to their toxicity toward rats, was analyzed by a fuzzy logic procedure called adaptive fuzzy partition (AFP). This method allows the establishment of molecular descriptor/chemical activity relationships by dynamically dividing the descriptor space into a set of fuzzily partitioned subspaces. A set of 153 molecular descriptors was analyzed, including topological, physicochemical, quantum mechanical, constitutional, and electronic parameters, and the most relevant descriptors were selected with the help of a procedure combining genetic algorithm concepts and a stepwise method. The ability of this AFP model to classify the three toxicity classes was validated after dividing the data set compounds into training and test sets, including 165 and 70 molecules, respectively. The experimental class was correctly predicted for 76% of the test-set compounds. Furthermore, the most toxic class, particularly important for real applications of the toxicity models, was correctly predicted in 86% of cases. Finally, a comparison between the results obtained by AFP and those obtained by other classic classification techniques showed that AFP improved the predictive power of the proposed models.