Energy balance of microalgae biofuels

Microalgae are small organisms that live in the water and use solar energy to grow. Like plants, they can be used to produce biofuels. Since the Second World War there have been repeated attempts to produce biofuels from microalgae. The idea has recently received a boost due to one specific feature of microalgae: unlike other biofuel feedstock, microalgae do not compete with food production for arable land. Biofuel production with microalgae is only sensible when less energy is required to produce the fuel than is stored in the fuel. The ratio of energy demand to energy output, the ‘Net Energy Ratio’ (NER), should be smaller than one. Previous studies have shown that the NER depends significantly on (a) the assumed operation energy, and (b) the expected biomass productivities. Although it is well-known that these two parameters are inherently linked, this dependency has not been considered when calculating the NER. In this dissertation, for the first time biomass productivity is calculated based on operation energy. For this purpose, a correlation between the key parameters to model operation energy and biomass productivity (aeration rate, light intensity and photosynthetic efficiency (PE)) is derived and validated based on a systematic analysis of published experimental data. Based on this correlation, the NER of microalgae biofuels production is calculated. Aerated flat plate photobioreactors are investigated as a method of microalgae cultivation. These have previously been examined as promising systems for outdoor cultivation. As a biofuel, biomethane production is investigated since its production requires the least energy compared to other biofuels. The results of this dissertation show that operation energy and biomass productivities are related non-linearly: to achieve high productivities, disproportionately more energy is required than to achieve low productivities. Consequently, the aim of energy-efficient microalgae cultivation is not to achieve the highest possible biomass yield but to find a good balance between operation energy and biomass yield. Furthermore, due to these interactions, the lowest possible NER is not achieved with the maximum biomass yield. The optimum NER depends on the interaction of all model parameters. The effect of parameter changes on the NER depends also on the aeration rate. The NER calculated in this dissertation for aerated flat plate photobioreactors is around 1.8. This value is achieved at an aeration rate of 0.25 vvm (gas volume gas per liquid volume and minute). This corresponds, when coupled with the further findings and assumptions of this study, to an operation power of 54 W m-3 or 2.2 W m-2 and a biomass productivity of 50 t ha-1 y-1. A NER below one could not be achieved even though expected technological improvement is considered in the calculation. The calculated NER is compared to the NER results in previous studies which were partially below one. The analysis of previous studies showed that there are two main reasons for a NER < 1: one is incomplete system boundaries; the other is that the relation between energy demand and productivity is not considered. With the systematic approach presented in this dissertation, the potential development of microalgae biofuel production can be predicted more reliably. Expected technological development could improve the relation between operation energy and biomass productivities, but it cannot uncouple these parameters. Their correlation is based on the fundamental principles of microalgae growth, which apply to all cultivation systems and all types of algae. The method developed in this thesis can also be applied to quantify the best possible NER for other cultivation systems, based on the relation between operation energy and biomass productivity. The approach to correlating important model parameters based on the underlying scientific mechanisms can be transferred to other systems as well. It can thus also be applied to estimate the potential development of other technologies

Physiological and Molecular Effects of in vivo and ex vivo Mild Skin Barrier Disruption

The success of topically applied treatments on skin relies on the efficacy of skin penetration. In order to increase particle or product penetration, mild skin barrier disruption methods can be used. We previously described cyanoacrylate skin surface stripping as an efficient method to open hair follicles, enhance particle penetration, and activate Langerhans cells. We conducted ex vivo and in vivo measurements on human skin to characterize the biological effect and quantify barrier disruption-related inflammation on a molecular level. Despite the known immunostimulatory effects, this barrier disruption and hair follicle opening method was well accepted and did not result in lasting changes of skin physiological parameters, cytokine production, or clinical side effects. Only in ex vivo human skin did we find a discrete increase in IP-10, TGF-β, IL-8, and GM-CSF mRNA. The data underline the safety profile of this method and demonstrate that the procedure per se does not cause substantial inflammation or skin damage, which is also of interest when applied to non-invasive sampling of biomarkers in clinical trials

Multifunctional polymer-capped mesoporous silica nanoparticles for pH-responsive targeted drug delivery

Supramolecular templating techniques have been widely used to direct the formation of porous materials with the goal of introducing permanent mesoporosity. While surfactant-directed self-assembly has been exploited for inorganic materials such as titania, silica, organosilica, and zeolites, it has rarely been applied to metal-organic frameworks (MOFs) and coordination polymers. Here we introduce a new family of gemini surfactant-directed zinc imidazolates, referred to as mesostructured imidazolate frameworks (MIFs), and present a detailed study on the influence of different gemini-type surfactants on the formation mechanism and structures of the resulting zinc imidazolates. The proposed formation mechanism for MIF-type materials involves co-assembly and crystallization processes that yield lamellar mesostructured imidazolate frameworks. Understanding and controlling such processes also has implications for the syntheses of microporous zinc imidazolate framework (ZIF) materials, whose formation can be suppressed in surfactant-rich solutions, whereas formation of MIF materials is favored in the presence of surfactants and triggered by the addition of halogenides. Solid-state 2D 13C1H HETCOR NMR measurements on prototypic CTAB-directed MIF-1 establish that the head group moieties of the surfactant molecules interact strongly with the zinc-imidazolate-bromide sheets. Additionally, the NMR analyses suggest that MIF-1 has a significant fraction of surfactant molecules that are interdigitated between the zinc-imidazolate-bromide sheets with an antiparallel stacking arrangement, consistent with the high thermal and chemical stability of the MIF hybrid materials

Multifunctional polymer-capped mesoporous silica nanoparticles for pH-responsive targeted drug delivery

Supramolecular templating techniques have been widely used to direct the formation of porous materials with the goal of introducing permanent mesoporosity. While surfactant-directed self-assembly has been exploited for inorganic materials such as titania, silica, organosilica, and zeolites, it has rarely been applied to metal-organic frameworks (MOFs) and coordination polymers. Here we introduce a new family of gemini surfactant-directed zinc imidazolates, referred to as mesostructured imidazolate frameworks (MIFs), and present a detailed study on the influence of different gemini-type surfactants on the formation mechanism and structures of the resulting zinc imidazolates. The proposed formation mechanism for MIF-type materials involves co-assembly and crystallization processes that yield lamellar mesostructured imidazolate frameworks. Understanding and controlling such processes also has implications for the syntheses of microporous zinc imidazolate framework (ZIF) materials, whose formation can be suppressed in surfactant-rich solutions, whereas formation of MIF materials is favored in the presence of surfactants and triggered by the addition of halogenides. Solid-state 2D 13C1H HETCOR NMR measurements on prototypic CTAB-directed MIF-1 establish that the head group moieties of the surfactant molecules interact strongly with the zinc-imidazolate-bromide sheets. Additionally, the NMR analyses suggest that MIF-1 has a significant fraction of surfactant molecules that are interdigitated between the zinc-imidazolate-bromide sheets with an antiparallel stacking arrangement, consistent with the high thermal and chemical stability of the MIF hybrid materials

Sorting of Golgi resident proteins into different subpopulations of COPI vesicles: a role for ArfGAP1

We present evidence for two subpopulations of coatomer protein I vesicles, both containing high amounts of Golgi resident proteins but only minor amounts of anterograde cargo. Early Golgi proteins p24α2, β1, δ1, and γ3 are shown to be sorted together into vesicles that are distinct from those containing mannosidase II, a glycosidase of the medial Golgi stack, and GS28, a SNARE protein of the Golgi stack. Sorting into each vesicle population is Arf-1 and GTP hydrolysis dependent and is inhibited by aluminum and beryllium fluoride. Using synthetic peptides, we find that the cytoplasmic domain of p24β1 can bind Arf GTPase-activating protein (GAP)1 and cause direct inhibition of ArfGAP1-mediated GTP hydrolysis on Arf-1 bound to liposomes and Golgi membranes. We propose a two-stage reaction to explain how GTP hydrolysis constitutes a prerequisite for sorting of resident proteins, yet becomes inhibited in their presence

Glycolipids produced by Rouxiella sp. DSM 100043 and isolation of the biosurfactants via foam-fractionation

Additional file 1. Table S1, Figure S1–Figure S3: Mass spectrometry data and plots of purified foam extracts of Rouxiella sp. DSM 100043. Figure S4: Full NMR spectra of Rouxiella sp. DMS 100043 glycolipids present in fractions 64-65

Guiding motives and storylines of the German energy transition: How to systematically integrate stakeholder positions into energy transformation pathways

Transformationspfade zu einem nachhaltigen Energiesystem beruhen meist auf modellbasierten Szenarien. In den Szenarien müssen gesellschaftliche Prozesse und deren Interaktion mit technologischen, ökonomischen und ökologischen Aspekten betrachtet werden. Dies setzt u.a. eine Integration zentraler Stakeholder-Positionen in die Szenarien voraus. Hierzu präsentieren wir Ansätze aus zwei Forschungsprojekten: Der erste Ansatz identifiziert gesellschaftliche Leitmotive der Energiewende und analysiert, in welchen technisch-ökonomischen Transformationspfaden diese realisiert werden können. Der zweite Ansatz setzt auf eine partizipative Entwicklung von Storylines, um eine verbesserte Legitimation und Kommunikation von Transformationspfaden zu erreichen. Wir diskutieren die Herangehensweisen beider Ansätze, die Positionen von Stakeholdern methodisch zu erfassen und mit technisch-ökonomischen Perspektiven zur Energiesystemtransformation zu verknüpfen.Pathways towards sustainable energy transformations are usually premised on model-based scenarios. These scenarios have to incorporate societal dynamics and their interaction with technological, economic, and ecological processes. This also requires the consideration and integration of the stakeholders’ positions into the scenarios. For this purpose, we present two different approaches: the first one identifies the stakeholders’ guiding motives regarding the German energy transition and analyzes how these positions can be realized in technological and economic transformation pathways. In the second approach, storylines are developed in a participatory process with the stakeholders to ensure increased legitimacy and communication of the resulting pathways. For both approaches, we discuss the methodological capture of stakeholder positions and their subsequent integration into energy transformation pathways

Imputation of missing values of tumour stage in population-based cancer registration

<p>Abstract</p> <p>Background</p> <p>Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.</p> <p>Methods</p> <p>This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.</p> <p>Results</p> <p>The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.</p> <p>Conclusions</p> <p>This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.</p