Eleftheria Dossi

Chiral smectic liquid-crystalline polyacrylates with variously spaced and substituted biphenylene units

Four new classes of Chiral liquid-crystalline polyacrylates A-n to D-n based on variously spaced and substituted biphenylene mesogens have been prepared. Spacer segments of n(= 5 – 11) methylene groups were used, and the chiral substituents were the (S)-2-methylbutoxy (A-n), (S)-1-methylheptyloxy (B-n), (S)-2-chloro-3-methylbutanoyloxy (C-n), or (2S,3S)-2-chloro-3-methylpentanoyloxy (D-n) groups. The polyacrylates exhibited a strong tendency to form the smectic A mesophase, sometimes in polymorphic sequences. In particular, on varying the length of the alkylene spacer of polyacrylates A-n the structure of the chiral smectic A phase evolved from bilayer (n⩽ 6) to interdigitated (n= 7) and to monolayer (n > 7). Depending on the nature of the chiral substituent, chiral smectic C and chiral nematic mesophases were also observed in a few samples. The polyacrylates appeared to be suitable candidates for studies of the electroclinic response in the chiral smectic A phase.

New Multifunctional Polymeric Materials for the Treatment of Chronic Uremia

The preparation and characterization of polymeric materials attained by synthetic or semisynthetic procedures is reported. These include radical copolymerization of acryloyl and methacryloyl monomers and modification of oligo- and polysaccharides. All the polymeric materials are characterized by hydrogel-like properties which are tunable by pH; the maximum swelling occurred under alkaline conditions. This behavior, combined with their capability to take up urea, ammonia and sodium and potassium ions, makes them good candidates for the removal of uremic toxins from patients affected by chronic uremia. The results of the in vitro evaluation of the efficacy of these materials to interact with the reported metabolites, including water, were analyzed in view of predicting their in vivo responses.

Heat of protonation of endoglucanase V catalytic domain (EGV-core) from Humicola insolens

Abstract Endoglucanase V catalytic core is a single chain protein of 213 amino acids. The titration of the ionizable groups of the macromolecule has been performed by an isothermal calorimetric technique and the pK0i and ΔH0i related to the groups have been estimated and no apparent abnormality was observed in the titration behavior. These values are in agreement with the expected theoretical ones.

Structural characterization of the smectic polymorphism of chiral liquid crystalline polysiloxanes containing biphenyl mesogens

Abstract A structural characterization of chiral side chain siloxanes with different average degrees of polymerization, DP n, was performed by X-ray diffraction experiments on powder and oriented fibre specimens. Polymers (DP n = 35) and oligomers (DP n = 4) contained the 4,4′-biphenylene unit with either an (S)-2-methylbutoxy (An, Bn) or an (S)-2-chloro-3-methylbutanoyloxy substituent (Cll). The spacer segment connected to the siloxane backbone had a variable number, n, of methylene groups (n = 5, 8, or 11). Independent of the spacer length and the chiral tail nature, the polysiloxanes underwent the same sequence of phases: C-SF1 (or SI1)-SC1-SA1-I, whereas in the oligosiloxanes the sequence C-SB1-SA1-I (B11) or C-SF1-SC1-I (B5) occurred. The influence of the structure of the polysiloxanes on the formation of the smectic (tilted or orthogonal) mesophases was elucidated. The rather large number of reflections (three or four) detected in the X-ray patterns at low angles, allowed a drawing of the projection...

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control trafficking of illegal drugs, explosive detection, or in other law enforcement applications.

CRIM-TRACK: Sensor System for Detection of Criminal Chemical Substances

Detection of illegal compounds requires a reliable, selective and sensitive detection device. The successful device features automated target acquisition, identification and signal processing. It is portable, fast, user friendly, sensitive, specific, and cost efficient. LEAs are in need of such technology. CRIM-TRACK is developing a sensing device based on these requirements. We engage highly skilled specialists from research institutions, industry, SMEs and LEAs and rely on a team of end users to benefit maximally from our prototypes. Currently we can detect minute quantities of drugs, explosives and precursors thereof in laboratory settings. Using colorimetric technology we have developed prototypes that employ disposable sensing chips. Ease of operation and intuitive sensor response are highly prioritized features that we implement as we gather data to feed into machine learning. With machine learning our ability to detect threat compounds amidst harmless substances improves. Different end users prefer their equipment optimized for their specific field. In an explosives-detecting scenario, the end user may prefer false positives over false negatives, while the opposite may be true in a drug-detecting scenario. Such decisions will be programmed to match user preference. Sensor output can be as detailed as the sensor allows. The user can be informed of the statistics behind the detection, identities of all detected substances, and quantities thereof. The response can also be simplified to “yes” vs. “no”. The technology under development in CRIM-TRACK will provide custom officers, police and other authorities with an effective tool to control trafficking of illegal drugs and drug precursors.

New Polymeric Hydrogels for the Removal of Uraemic Toxins

New polymeric hydrogels were prepared by radical copolymerization of methacrylic acid with acrylamides and oligo(oxyethylene)glycol mono- and dimethacrylates. Semisynthetic polymers containing a large number of aldehyde groups and in some case acid groups were also prepared by chemical modification of natural polysaccharides. “In vitro” tests have shown that the prepared matrices are characterized by a large capacity of water uptake, tunable by pH modulation, accompanied by a parallel ability to uptake urea, ammonia, sodium and potassium, matching the requirements for their utilization as adjuvants in the therapy of uraemic patients.