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The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives. We demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers, and show how the synthesis process can be efficiently directed to achieve material and process objectives.

The surface-enhanced Raman scattering (SERS) of sodium alginates and their hetero-and homopolymeric fractions obtained from four seaweeds of the Chilean coast was studied. Alginic acid is a copolymer of β-D-mannuronic acid (M) and α-L guluronic acid (G), linked 1 → 4, forming two homopolymeric fractions (MM and GG) and a heteropolymeric fraction (MG). The SERS spectra were registered on silver colloid with the 632.8 nm line of a He-Ne laser. The SERS spectra of sodium alginate and the polyguluronate fraction present various carboxylate bands which are probably due to the coexistence of different molecular conformations. SERS allows to differentiate the hetero-and homopolymeric fractions of alginic acid by characteristic bands. In the fingerprint region, all the poly-D-mannuronate samples present a band around 946 cm −1 assigned to C-O stretching, and C-C-H and C-O-H deformation vibrations, a band at 863 cm−1 assigned to deformation vibration of β-C 1 -H group, and one at 799-788 cm −1 due to the contributions of various vibration modes. Poly-L-guluronate spectra show three characteristic bands, at 928-913 cm −1 assigned to symmetric stretching vibration of C-O-C group, at 890-889 cm −1 due to C-C-H, skeletal C-C, and C-O vibrations, and at 797 cm −1 assigned to α C 1 -H deformation vibration. The heteropolymeric fractions present two characteristic bands in the region with the more important one being an intense band at 730 cm −1 due to ring breathing vibration mode.

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The tetrasaccharide of 1 → 4 β-D-mannopyranuronate (MM) and the alternating tetrasaccharide of 1 → 4 b-Dmannopyranuronate and 1 → 4 α-L-gulopyranuronate (MG) were analyzed based on density functional theory (DFT) by employing the Gaussian 03 W package. The molecular geometries were fully optimized by using the Becke's three-parameter hybrid exchange functional combined with Lee-Yang-Parr correlation functional (B3LYP) and using a 6-31G(d,p) basis set. The calculated IR spectrum of MM presents a band at 1093 cm −1 for C-C stretching vibration, which is in good agreement with the experimental observation (1096 cm −1 ) for the polymannuronate fraction obtained by partial hydrolysis of sodium alginate extracted from the hybrid brown seaweed Lessonia-Macrocystis. The calculated value at 826 cm −1 for MM is in close agreement with the experimental value and confirms that this band is characteristic of polymannuronate blocks. Most of the bands in the IR spectrum are also present in the observed Raman spectrum of the polymannuronate fraction. The experimental IR spectrum of heteropolymeric fraction obtained by partial hydrolysis of sodium alginate shows absorbances similar to those calculated for the model tetrasaccharide (MG). Surface-enhanced Raman scattering (SERS) allows differentiation between the homopolymeric and heteropolymeric fractions of sodium alginate. The SERS spectrum of the heteropolymeric fraction shows an enhanced signal at 731 cm −1 which is present in the calculated Raman spectrum of the tetrasaccharide MG at 729 cm −1 . This band is assigned to the ring-breathing deformation of the β-D-mannopyranuronate and α-L-gulopyranuronate residues.

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Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that changes over time. Such problems can be formulated as a functional optimisation problem. We develop a novel Bayesian optimisation framework for such functional optimisation of expensive blackbox processes. We represent the control function using Bernstein polynomial basis and optimise in the coefficient space. We derive the theory and practice required to dynamically adjust the order of the polynomial degree, and show how prior information about shape can be integrated. We demonstrate the effectiveness of our approach for short polymer fibre design and optimising learning rate schedules for deep networks. n v=0 αvbv,n(t), where α =

This work is related to the structural characterization of the sulfated polysaccharide from Lessonia sp and the study of its antioxidant and antiparasitic properties. Sequential extraction afforded D-mannitol as the only low MW sugar alcohol. Extraction with 2% CaCl 2 afforded in 3.0% yield, a sulfated fucan (SF). Its major fraction (48.5% yield), isolated by ion-exchange chromatography corresponds to a linear polymer of α-l-fucopyranosil residues linked 1→3, sulfated at the O-4 and partially at O-2 positions. By alkaline extraction, sodium alginate (10.3% yield) was obtained. The antioxidant capacity of SF by ESR showed high elimination index (IC 50 , mg/mL) of hydroxyl (0.27), alkoxy (10.05), and peroxyl (82.88) radicals in relation to commercial mannitol. SF showed activity against the epimastigote form of Trypanosoma cruzi parasite (250 µg/mL) and low cytotoxicity in murine cells (367 µg/mL). The elimination capacity of radicals in aqueous medium of SF would allow its potential biomedical application.