Characterization of a photonic strain sensor in silicon-on-insulator technology

Recently there has been growing interest in sensing by means of optical microring resonators in photonic integrated circuits that are fabricated in silicon-on-insulator (SOI) technology. Taillaert et al. [Proc. SPIE 6619, 661914 (2007)] proposed the use of a silicon-waveguide-based ring resonator as a strain gauge. However, the strong lateral confinement of the light in SOI waveguides and its corresponding modal dispersion where not taken into account. We present a theoretical understanding, as well as experimental results, of strain applied on waveguide-based microresonators, and find that the following effects play important roles: elongation of the racetrack length, modal dispersion of the waveguide, and the strain-induced change in effective refractive index.     

On Epistemics in Expected Free Energy for Linear Gaussian State Space Models

Active Inference (AIF) is a framework that can be used both to describe information processing in naturally intelligent systems, such as the human brain, and to design synthetic intelligent systems (agents). In this paper we show that Expected Free Energy (EFE) minimisation, a core feature of the framework, does not lead to purposeful explorative behaviour in linear Gaussian dynamical systems. We provide a simple proof that, due to the specific construction used for the EFE, the terms responsible for the exploratory (epistemic) drive become constant in the case of linear Gaussian systems. This renders AIF equivalent to KL control. From a theoretical point of view this is an interesting result since it is generally assumed that EFE minimisation will always introduce an exploratory drive in AIF agents. While the full EFE objective does not lead to exploration in linear Gaussian dynamical systems, the principles of its construction can still be used to design objectives that include an epistemic drive. We provide an in-depth analysis of the mechanics behind the epistemic drive of AIF agents and show how to design objectives for linear Gaussian dynamical systems that do include an epistemic drive. Concretely, we show that focusing solely on epistemics and dispensing with goal-directed terms leads to a form of maximum entropy exploration that is heavily dependent on the type of control signals driving the system. Additive controls do not permit such exploration. From a practical point of view this is an important result since linear Gaussian dynamical systems with additive controls are an extensively used model class, encompassing for instance Linear Quadratic Gaussian controllers. On the other hand, linear Gaussian dynamical systems driven by multiplicative controls such as switching transition matrices do permit an exploratory drive.     

Paving the way to conformationally unravel complex glycopeptide antibiotics by means of Raman optical activity

It is crucial for fundamental physical chemistry techniques to find their application in tackling real-world challenges. Hitherto, Raman optical activity (ROA) spectroscopy is one of the examples where a promising future within the pharmaceutical sector is foreseen, but has not yet been established. Namely, the technique is believed to be able to contribute in investigating the conformational behaviour of drug candidates. We, herein, strive towards the alignment of the ROA analysis outcome and the pharmaceutical expectations by proposing a fresh strategy that ensures a more complete, reliable, and transferable ROA study. The strategy consists of the treatment of the conformational space by means of a principal component analysis (PCA) and a clustering algorithm, succeeded by a thorough ROA spectral analysis and a novel way of estimating the contributions of the different chemical fragments to the total ROA spectral intensities. Here, vancomycin, an antibiotic glycopeptide, has been treated; it is the first antibiotic glycopeptide studied by means of ROA and is a challenging compound in ROA terms. By applying our approach we discover that ROA is capable of independently identifying the correct conformation of vancomycin in aqueous solution. In addition, we have a clear idea of what ROA can and cannot tell us regarding glycopeptides. Finally, the glycopeptide class turns out to be a spectroscopically curious case, as its spectral responses are unlike the typical ROA spectral responses of peptides and carbohydrates. This preludes future ROA studies of this intriguing molecular class.

Raman optical activity tackles the complex conformational space of glycopeptide antibiotics.     

13C NMR Study of the grafting of 13C labeled maleic anhydride onto PE,PP and EPM

The chemical structure of polyolefins grafted with maleic anhydride (MA) has been the subject of much speculation, but thorough experimental studies are rare. MA with 99% ¹³C in the double bond was synthesized and grafted onto PE, EPM and PP in the melt and solution. 1D INADEQUATE ¹³C NMR spectroscopy was used to characterize the products. Saturated, monomeric MA graft structures are formed. Only for grafted PE short MA oligomers are demonstrated. Grafting occurs on secondary and tertiary carbons depending on the composition of the polyolefin. For PP a new, unsaturated MA graft structure on the polymer chain terminus is identified. All graft structures are rationalized using a simple grafting mechanism.     

Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models

Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning.     

Poly(N-isopropylacrylamide) with hydrolyzable lactic acid ester side groups: a new type of thermosensitive polymer

N-isopropylacrylamide (NIPAAm) was copolymerized with 2-hydroxyethyl methacrylate-monolactate (HEMA-monolactate). With increasing HEMA-monolactate content in the copolymer, the lower critical solution temperature (LCST) decreases. During incubation in an aqueous solution, the lactate groups are released by hydrolysis, by which the copolymer is converted into poly(NIPAAm-co-HEMA). By this process, the hydrophilicity of the copolymer increases, resulting in increased LCST values.     

Thermo‐Responsive Solid‐Phase Extraction Using Poly(methyl vinyl ether)–Containing Segmented Polymer Networks

Segmented polymer networks containing poly(methyl vinyl ether) (PMVE) segments were prepared by free‐radical‐initiated copolymerization of PMVE‐α,ω‐dimethacrylate with styrene or 2‐hydroxyethyl methacrylate (HEMA). These networks were evaluated as thermo‐responsive solid‐phase extraction materials. Suspension‐derived polymer networks consisting of 80% of PMVE and 20% of HEMA adsorb toluene from an aqueous solution at 40°C and release the adsorbed toluene quantitatively at 20°C.     

The sol-gel approach towards thermo-responsive poly(N-isopropyl acrylamide) hydrogels with improved mechanical properties

A new type of thermo-responsive hydrogels based on the polymer poly(N-isopropyl acrylamide) (PNIPAA) has been synthesized with the sol-gel technology. For the preparation of this type of nano-structured hydrogels, the inorganic silica phase was synthesized by the sol-gel process in the presence of an aqueous solution of high molecular weight PNIPAA. This combination of the organic and inorganic phases forms hybrid hydrogels with a semi-IPN morphology. The unique structure of these hydrogels improves the mechanical stability to a great extent as compared to conventional PNIPAA-hydrogels. This was shown by stress-strain experiments and the capability to absorb and desorb large amounts of water. The silica only slightly influences the transition temperature of the hydrogels but allows us to vary the thermo-responsive properties of the materials to a great extent.     

Polymer networks containing crystallizable poly(octadecyl vinyl ether) segments for shape‐memory materials

New polymer networks were prepared by free radical initiated copolymerization of crystallizable α,ω‐bismethacrylate‐terminated poly(octadecyl vinyl ether) (polyODVE) with butyl acrylate (BA). The polyODVE bismacromonomers were obtained by end‐capping the bifunctionally living cationic polymerization of ODVE with 2‐hydroxyethyl methacrylate (HEMA). The segmented networks show a high degree of phase separation over a wide range of compositions. The shape memory properties of a material containing 20 wt.‐% of polyODVE are reported.