Modeling control and transduction of electrochemical gradients in acid-stressed bacteria.

Transmembrane electrochemical gradients drive solute uptake and constitute a substantial fraction of the cellular energy pool in bacteria. These gradients act not only as "homeostatic contributors," but also play a dynamic and keystone role in several bacterial functions, including sensing, stress response, and metabolism. At the system level, multiple gradients interact with ion transporters and bacterial behavior in a complex, rapid, and emergent manner; consequently, experiments alone cannot untangle their interdependencies. Electrochemical gradient modeling provides a general framework to understand these interactions and their underlying mechanisms. We quantify the generation, maintenance, and interactions of electrical, proton, and potassium potential gradients under lactic acid-stress and lactic acid fermentation. Further, we elucidate a gradient-mediated mechanism for intracellular pH sensing and stress response. We demonstrate that this gradient model can yield insights on the energetic limitations of membrane transport, and can predict bacterial behavior across changing environments.

Vapor-fed bio-hybrid fuel cell.

BACKGROUND: Concentration and purification of ethanol and other biofuels from fermentations are energy-intensive processes, with amplified costs at smaller scales. To circumvent the need for these processes, and to potentially reduce transportation costs as well, we have previously investigated bio-hybrid fuel cells (FCs), in which a fermentation and FC are closely coupled. However, long-term operation requires strictly preventing the fermentation and FC from harming each other. We introduce here the concept of the vapor-fed bio-hybrid FC as a means of continuously extracting power from ongoing fermentations at ambient conditions. By bubbling a carrier gas (N2) through a yeast fermentation and then through a direct ethanol FC, we protect the FC anode from the catalyst poisons in the fermentation (which are non-volatile), and also protect the yeast from harmful FC products (notably acetic acid) and from build-up of ethanol. RESULTS: Since vapor-fed direct ethanol FCs at ambient conditions have never been systematically characterized (in contrast to vapor-fed direct methanol FCs), we first assess the effects on output power and conversion efficiency of ethanol concentration, vapor flow rate, and FC voltage. The results fit a continuous stirred-tank reactor model. Over a wide range of ethanol partial pressures (2-8 mmHg), power densities are comparable to those for liquid-fed direct ethanol FCs at the same temperature, with power densities >2 mW/cm2 obtained. We then demonstrate the continuous operation of a vapor-fed bio-hybrid FC with fermentation for 5 months, with no indication of performance degradation due to poisoning (of either the FC or the fermentation). It is further shown that the system is stable, recovering quickly from disturbances or from interruptions in maintenance. CONCLUSIONS: The vapor-fed bio-hybrid FC enables extraction of power from dilute bio-ethanol streams without costly concentration and purification steps. The concept should be scalable to both large and small operations and should be generalizable to other biofuels and waste-to-energy systems.

Simple, fast, and accurate methodology for quantitative analysis using Fourier transform infrared spectroscopy, with bio-hybrid fuel cell examples.

The standard methodologies for quantitative analysis (QA) of mixtures using Fourier transform infrared (FTIR) instruments have evolved until they are now more complicated than necessary for many users' purposes. We present a simpler methodology, suitable for widespread adoption of FTIR QA as a standard laboratory technique across disciplines by occasional users.•Algorithm is straightforward and intuitive, yet it is also fast, accurate, and robust.•Relies on component spectra, minimization of errors, and local adaptive mesh refinement.•Tested successfully on real mixtures of up to nine components. We show that our methodology is robust to challenging experimental conditions such as similar substances, component percentages differing by three orders of magnitude, and imperfect (noisy) spectra. As examples, we analyze biological, chemical, and physical aspects of bio-hybrid fuel cells.