Light-Addressable Potentiometric Sensors in Microfluidics

Light-addressable potentiometric sensor (LAPS) is an electrochemical sensor based on the field-effect principle of semiconductor. It is able to sense the change of Nernst potential on the sensor surface, and the measuring area can be controlled by the illumination. Due to the unique light-addressable ability of LAPS, the chemical imaging system constructed with LAPS can realize the two-dimensional image distribution detection of chemical/biomass. In this paper, the advantages of LAPS as sensing unit of microelectrochemical analysis system are summarized. Then, the greatest development of LAPS analysis system is explained and discussed. Especially, this paper focused on the research of ion diffusion, enzymatic reaction, microbial metabolism and droplet microfluidics by using LAPS analysis system. Finally, the development trends and prospects of LAPS analysis system are illustrated.

An Examination of the Catalyst Layer Contribution to the Disparity between the Nernst Potential and Open Circuit Potential in Proton Exchange Membrane Fuel Cells

The dependency of the Nernst potential in an operating proton exchange membrane fuel cell (PEMFC) on the temperature, inlet pressure and relative humidity (RH) is examined, highlighting the synergistic dependence of measured open circuit potential (OCP) on all three parameters. An alternative model of the Nernst equation is derived to more appropriately represent the PEMFC system where reactant concentration is instead considered as the activity. Ex situ gas diffusion electrode (GDE) measurements are used to examine the dependency of temperature, electrolyte concentration, catalyst surface area and composition on the measured OCP in the absence of H2 crossover. This is supported by single-cell OCP measurements, wherein RH was also investigated. This contribution provides clarity on the parameters that affect the practically measured OCP as well as highlighting further studies into the effects of catalyst particle surrounding environment on OCP as a promising way of improving PEMFC performance in the low current density regime.

Towards the Understanding of Water-in-Salt Electrolytes: Individual Ion Activities and Liquid Junction Potentials in Highly Concentrated Aqueous Solutions

Highly concentrated electrolytes were recently proposed to improve the performances of aqueous electrochemical systems by delaying the water splitting and increasing the operating voltage for battery applications. While advances were made regarding their implementation in practical devices, debate exists regarding the physical origin for the delayed water reduction occurring at the electrode/electrolyte interface. Evidently, one difficulty resides in our lack of knowledge regarding ions activity arising from this novel class of electrolyte, it being necessary to estimate the Nernst potential of associated redox reactions such as Li⁺ intercalation or the hydrogen evolution reaction. In this work, we first measured the potential shift of electrodes selective to either Li⁺, H⁺ or Zn²⁺ ions from diluted to highly concentrated regimes in LiCl or LiTFSI solutions. Observing similar shifts for these different cations and environments, we establish that shifts in redox potentials from diluted to highly concentrated regime originates in large from an increase junction potential, it being dependent on the ions activity coefficients that increase with concentration. While our study shows that single ion activity coefficients, unlike mean ion activity coefficients, cannot be captured by any electrochemical means, we demonstrate that protons concentration increases by approximatively two orders of magnitude from 1 mol.kg^-1 to 15-20 mol.kg^-1 solutions. Combined with the increased activity coefficients, this increases the activity of protons and thus the pH of highly concentrated solutions which appears acidic.

Towards the Understanding of Water-in-Salt Electrolytes: Individual Ion Activities and Liquid Junction Potentials in Highly Concentrated Aqueous Solutions

Highly concentrated electrolytes were recently proposed to improve the performances of aqueous electrochemical systems by delaying the water splitting and increasing the operating voltage for battery applications. While advances were made regarding their implementation in practical devices, debate exists regarding the physical origin for the delayed water reduction occurring at the electrode/electrolyte interface. Evidently, one difficulty resides in our lack of knowledge regarding ions activity arising from this novel class of electrolyte, it being necessary to estimate the Nernst potential of associated redox reactions such as Li⁺ intercalation or the hydrogen evolution reaction. In this work, we first measured the potential shift of electrodes selective to either Li⁺, H⁺ or Zn²⁺ ions from diluted to highly concentrated regimes in LiCl or LiTFSI solutions. Observing similar shifts for these different cations and environments, we establish that shifts in redox potentials from diluted to highly concentrated regime originates in large from an increase junction potential, it being dependent on the ions activity coefficients that increase with concentration. While our study shows that single ion activity coefficients, unlike mean ion activity coefficients, cannot be captured by any electrochemical means, we demonstrate that protons concentration increases by approximatively two orders of magnitude from 1 mol.kg^-1 to 15-20 mol.kg^-1 solutions. Combined with the increased activity coefficients, this increases the activity of protons and thus the pH of highly concentrated solutions which appears acidic.

Deep Learning Method to Remove Chemical, Kinetic and Electric Artifacts on ISEs

We suggest a deep learning based sensor signal processing method to remove chemical, kinetic and electrical artifacts from ion selective electrodes’ measured values. An ISE is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, application of ISE on a mixture of multiple ion has some problem. First problem is a chemical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is the kinetic artifact caused by the movement of the liquid. Water molecules collide with the glass membrane causing abnormal peak values of voltage. The last artifact is the interference of ISEs. When multiple ISEs are dipped into same solution, one electrode’s signal emission interference voltage measurement of other electrodes. Therefore, an ISE is recommended to be applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both 3 artifacts at the same time. The proposed method used 5 layers of artificial neural networks to regress correct signal to remove complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R2 of regression was 0.997. A randomly chosen value of AI-processed data has MAPE less than 5% (p-value 0.016).

Pan-artifact Removing with Deep Learning, on ISEs

This paper presents a signal-processing method to remove pan-artifact on ISEs with artificial neural networks. An Ion Selective Electrode is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, Application of ISE on a multi-ion solution has problem. First problem is a chemicophysical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is that movement of liquid directly interfere the glass membrane, causing inaccurate voltage measurement. When multiple ISEs are dipped into same solution, a sensor’s signal emission interference voltage measurement of other sensors. Therefore, an ISE is recommended to applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both artifacts at the same time. The proposed method is designed to remove complex artifacts with one-shot calculation, with MAPE less than 1.8%, and R2 as 0.997. A randomly chosen value of AI-predicted value has MAPE less than 5% (p-value 0.016).

Escherichia coli’sphysiology can turn membrane voltage dyes into actuators

ABSTRACTThe electrical membrane potential (Vm) is one of the components of the electrochemical potential of protons across the biological membrane (proton motive force), which powers many vital cellular processes, andVmalso plays a role in signal transduction. Therefore, measuring it is of great interest, and over the years a variety of techniques has been developed for the purpose. In bacteria, given their small size, Nernstian membrane voltage probes are arguably the favourite strategy, and their cytoplasmic accumulation depends onVmaccording to the Nernst equation. However, a careful calibration of Nernstian probes that takes into account the trade-offs between the ease with which the signal from the dye is observed, and the dyes’ interactions with cellular physiology, is rarely performed. Here we use a mathematical model to understand such trade-offs and, based on the knowledge gained, propose a general work-flow for the characterization of Nernstian dye candidates. We demonstrate the work-flow on the Thioflavin T dye inEscherichia coli, and identify conditions in which the dye turns from aVmprobe into an actuator.SIGNIFICANCE STATEMENTThe phospholipid bilayer of a biological membrane is virtually impermeable to charged molecules. Much like in a rechargeable battery, cells harness this property to store an electrical potential that fuels life reactions but also transduces signals. Measuring this electrical potential, also referred to as membrane voltage, is therefore of great interest and a variety of techniques have been employed for the purpose, starting as early as the 1930s. For the case of bacteria, which are smaller in size and possess a stiffer cell wall, arguably the most popular approach to measuring membrane voltage are Nernstian probes that accumulate across the bacterial membrane according to the Nernst potential. The present study characterizes the undesired effects Nernstian probes can have on cell physiology, which can be crucial for the accurate interpretation of experimental results. Using mathematical modelling and experiments, the study provides a general, simple workflow to characterise and minimise these effects.

The Neuron at Rest

Neuronal membrane potential depends on the distribution of ions across the plasma membrane and the permeability of the membrane to those ions afforded by transmembrane proteins. Ions cannot pass through a lipid bilayer but enter or exit neurons through ion channels. When activated by voltage or a ligand, ion channels open to form a pore through which selective ions can pass. The ion channels that support a resting membrane potential are critical to setting a cell’s excitability. From the distribution of an ionic species, the Nernst potential can be used to predict the steady-state potential for that one ion. Neurons are permeable to potassium, sodium, and chloride ions at rest. The Goldman-Hodgkin-Katz equation takes into consideration the influence of multiple ionic species and can be used to predict neuronal membrane potential. Finally, how synaptic inputs affect neurons through synaptic currents and changes in membrane resistance is described.