Two new methods based on FT-Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band intensity ratio of the 380 and 1,096 cm -1 bands. For calibration purposes, 80.5% crystalline and 120-min milled (0% crystalline) Whatman CC31 and six cellulose mixtures produced with crystallinities in the range 10.9-64% were used. When intensity ratios were plotted against crystallinities of the calibration set samples, the plot showed a linear correlation (coefficient of determination R 2 = 0.992). Average standard error calculated from replicate Raman acquisitions indicated that the cellulose Raman crystallinity model was reliable. Crystallinities of the cellulose mixtures samples were also calculated from X-ray diffractograms using the amorphous contribution subtraction (Segal) method and it was found that the Raman model was better. Additionally, using both Raman and X-ray techniques, sample crystallinities were determined from partially crystalline cellulose samples that were generated by grinding Whatman CC31 in a vibratory mill. The two techniques showed significant differences. In the second approach, successful Raman PLS regression models for crystallinity, covering the 0-80.5% range, were generated from the ten calibration set Raman spectra. Both univariateRaman and WAXS determined crystallinities were used as references. The calibration models had strong relationships between determined and predicted crystallinity values (R 2 = 0.998 and 0.984, for univariateRaman and WAXS referenced models, respectively). Compared to WAXS, univariate-Raman referenced model was found to be better (root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values of 6.1 and 7.9% vs. 1.8 and 3.3%, respectively). It was concluded that either of the two Raman methods could be used for cellulose I crystallinity determination in cellulose samples.

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Although many enzymes can readily and selectively use oxygen in water-the most familiar and attractive of all oxidants and solvents, respectively-the design of synthetic catalysts for selective water-based oxidation processes utilizing molecular oxygen remains a daunting task. Particularly problematic is the fact that oxidation of substrates by O2 involves radical chemistry, which is intrinsically non-selective and difficult to control. In addition, metallo-organic catalysts are inherently susceptible to degradation by oxygen-based radicals, while their transition-metal-ion active sites often react with water to give insoluble, and thus inactive, oxides or hydroxides. Furthermore, pH control is often required to avoid acid or base degradation of organic substrates or products. Unlike metallo-organic catalysts, polyoxometalate anions are oxidatively stable and are reversible oxidants for use with O2 (refs 8,9,10). Here we show how thermodynamically controlled self-assembly of an equilibrated ensemble of polyoxometalates, with the heteropolytungstate anion [AIVVW11O40]6- as its main component, imparts both stability in water and internal pH-management. Designed to operate at near-neutral pH, this system facilitates a two-step O2-based process for the selective delignification of wood (lignocellulose) fibres. By directly monitoring the central Al atom, we show that equilibration reactions typical of polyoxometalate anions keep the pH of the system near 7 during both process steps.