4 research outputs found
An Experimental Approach to Systematically Probe Charge Inversion in Nanofluidic Channels
Charge inversion
of the surfaces of nanofluidic channels occurs
in systems with high-surface charge and/or highly charged ions and
is of particular interest because of applications in biological and
energy conversion systems. However, the details of such charge inversion
have not been clearly elucidated. Specifically, although we can experimentally
and theoretically show charge inversion, understanding at what conditions
charge inversion occurs, as well how much the charge-inverting ions
change the surface, are not known. Here, we show a novel experimental
approach for uniquely finding both the ζ-potential and adsorption
time of charge inverting ions in aqueous nanofluidic systems
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra
Surface-enhanced Raman spectroscopy
(SERS) has been well explored
as a highly effective characterization technique that is capable of
chemical pollutant detection and identification at very low concentrations.
Machine learning has been previously used to identify compounds based
on SERS spectral data. However, utilization of SERS to quantify concentrations,
with or without machine learning, has been difficult due to the spectral
intensity being sensitive to confounding factors such as the substrate
parameters, orientation of the analyte, and sample preparation technique.
Here, we demonstrate an approach for predicting the concentration
of sample pollutants from SERS spectra using machine learning. Frequency
domain transform methods, including the Fourier and Walsh–Hadamard
transforms, are applied to spectral data sets of three analytes (rhodamine
6G, chlorpyrifos, and triclosan), which are then used to train machine
learning algorithms. Using standard machine learning models, the concentration
of the sample pollutants is predicted with >80% cross-validation
accuracy
from raw SERS data. A cross-validation accuracy of 85% was achieved
using deep learning for a moderately sized data set (∼100 spectra),
and 70–80% was achieved for small data sets (∼50 spectra).
Performance can be maintained within this range even when combining
various sample preparation techniques and environmental media interference.
Additionally, as a spectral pretreatment, the Fourier and Hadamard
transforms are shown to consistently improve prediction accuracy across
multiple data sets. Finally, standard models were shown to accurately
identify characteristic peaks of compounds via analysis of their importance
scores, further verifying their predictive value
(Almost) Stationary Isotachophoretic Concentration Boundary in a Nanofluidic Channel Using Charge Inversion
The
present work is an experimental study of a new means to induce
a quasi-stationary boundary for concentration or separation in a nanochannel
induced by charge inversion. Instead of using pressure-driven counter-flow
to keep the front stationary, we exploit charge inversion by a highly
charged electrolyte, RuÂ(bpy)<sub>3</sub>Cl<sub>2</sub>, that changes
the sign of the zeta potential in part of the channel from negative
to positive. Having a non-charge inverting electrolyte (MgCl<sub>2</sub>) in the other part of the channel and applying an electric field
can create a standing front at the interface between them without
added dispersion due to an externally applied pressure-driven counterflow.
The resulting slow moving front position can be easily imaged optically
since RuÂ(bpy)<sub>3</sub>Cl<sub>2</sub> is fluorescent. A simple analytical
model for the velocity field and front axial position that reproduces
the experimental location of the front shows that the location can
be tuned by changing the concentration of the electrolytes (and thus
local zeta potential). Both of these give the charge inversion-mediated
boundary significant advantages over current methods of concentration
and separation and the method is, therefore, of particular importance
to chemical and biochemical analysis systems such as chromatography
and separations and for enhancing the stacking performance of field
amplified sample injection and isotachophoresis. By choosing a non-charge
inverting electrolyte other than MgCl<sub>2</sub>, either this electrolyte
or the RuÂ(bpy)<sub>3</sub>Cl<sub>2</sub> solution can be made to be
the leading or trailing electrolyte
Hybridization Thermodynamics of DNA Oligonucleotides during Microchip Capillary Electrophoresis
Capillary
electrophoresis (CE) is a powerful analytical tool for performing
separations and characterizing properties of charged species. For
reacting species during a CE separation, local concentrations change
leading to nonequilibrium conditions. Interpreting experimental data
with such nonequilibrium reactive species is nontrivial due to the
large number of variables involved in the system. In this work we
develop a COMSOL multiphysics-based numerical model to simulate the
electrokinetic mass transport of short interacting ssDNAs in microchip
capillary electrophoresis. We probe the importance of the dissociation
constant, <i>K</i><sub>D</sub>, and the concentration of
DNA on the resulting observed mobility of the dsDNA peak, μ<sub>w</sub>, by using a full sweep of parametric simulations. We find
that the observed mobility is strongly dependent on the DNA concentration
and <i>K</i><sub>D</sub>, as well as ssDNA concentration,
and develop a relation with which to understand this dependence. Furthermore,
we present experimental microchip capillary electrophoresis measurements
of interacting 10 base ssDNA and its complement with changes in buffer
ionic strength, DNA concentration, and DNA sequence to vary the system
equilibria. We then compare our results to thermodynamically calculated <i>K</i><sub>D</sub> values