Anionic polyalkoxy group comprising surfactants on basis of guerbet-alcohols, method of manufacture and use in enhanced oil recovery (EOR) applications

Compositions and methods of synthesis of anionic surfactants by alkoxylation of a Guerbet alcohol (GA) having 12 to 36 carbon atoms using butylene oxide, and optionally propylene oxide and/or ethylene oxide followed by the incorporation of a terminal anionic group are described herein. The GA of the present invention is made by a facile and inexpensive method that involves high temperature base catalyzed dimerization of alcohols with 6 to 18 carbon atoms. The large hydrophobe ether surfactants of the present invention find uses in enhanced oil recovery (EOR) applications where it is used for solubilization and mobilization of oil and for environmental cleanup. Further, the hydrophobe alkoxylated GA without anionic terminal group can be used as an ultra-high molecular weight non-ionic surfactant.Board of Regents, University of Texas Syste

Anionic polyalkoxy group comprising surfactants on basis of guerbet-alcohols, method of manufacture and use in enhanced oil recovery (EOR) applications

Compositions and methods of synthesis of anionic surfactants by alkoxylation of a Guerbet alcohol (GA) having 12 to 36 carbon atoms using butylene oxide, and optionally propylene oxide and/or ethylene oxide followed by the incorporation of a terminal anionic group are described herein. The GA of the present invention is made by a facile and inexpensive method that involves high temperature base catalyzed dimerization of alcohols with 6 to 18 carbon atoms. The large hydrophobe ether surfactants of the present invention find uses in enhanced oil recovery (EOR) applications where it is used for solubilization and mobilization of oil and for environmental cleanup. Further, the hydrophobe alkoxylated GA without anionic terminal group can be used as an ultra-high molecular weight non-ionic surfactant.Board of Regents, University of Texas Syste

The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data

I want to monitor my athlete but where do I start? Given the relationships among athlete workloads, injury 1 and performance, 2 athlete monitoring has become critical in the high-performance sporting environment. Sports medicine and science staff have a suite of monitoring tools available to track how much ‘work’ an athlete has performed, the response to that ‘work’ and whether the athlete is in a relative state of fitness or fatigue. The volume of literature, coupled with clever marketing around the ‘best approaches’ to optimising athlete performance, has resulted in practitioners having more choices than ever before. Furthermore, the range of different practices used in sport and the lack of agreement between parties emphasise the importance of having a clear rationale for athlete monitoring. The aim of this paper is to provide a practical guide to strategic planning, analysing, interpreting and applying athlete monitoring data in the sporting environment irrespective of data management software

Phenology and classification of abandoned agricultural land based on ALOS-1 and 2 PALSAR multi-temporal measurements

Agricultural crop abandonment negatively impacts local economy and environment since land, as a resource for agriculture, is not optimally utilized. To take necessary actions to rehabilitate abandoned agricultural lands, the identification of the spatial distribution of these lands must be acknowledged. While optical images had previously illustrated potentials in the identification of agricultural land abandonment, tropical areas often suffer cloud coverage problem that limits the availability of the imageries. Therefore, this study was conducted to investigate the potential of ALOS-1 and 2 (Advanced Land Observing Satellite-1 and 2) PALSAR (Phased Array L-band Synthetic Aperture Radar) images for the identification and classification of abandoned agricultural crop areas, namely paddy, rubber and oil palm fields. Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR; nonetheless, oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes. The accuracy obtained for these abandoned lands of paddy, rubber and oil palm was 93.33% ± 0.06%, 78% ± 2.32% and 63.33% ± 1.88%, respectively. This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops. The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas