The method presented here provides a direct way to determine mercury in tap water samples at the parts-per-trillion level. Its outstanding selectivity and sensitivity results from the well-known amalgamation process that occurs between mercury and gold. The entire procedure takes less than 10 min. No sample separation or sample preconcentration is required. The only step prior to mercury determination consists of mixing the water sample with a gold nanorod solution in sodium borohydride. The analytical figures of merit demonstrate precise and accurate analysis at the parts-per-trillion level. The limit of detection (6.6 x 10(-13) g x L(-1)) shows excellent potential for monitoring ultralow levels of mercury in water samples.
Service-learning (SL) is an active
learning approach that connects
the knowledge a student acquires in the classroom to an application
that benefits the community. Increasingly popular in the chemistry
curriculum, service-learning is reported to provide student benefits
including improved cognitive goals; increased academic, interpersonal,
and leadership skills; increased ability to apply course concepts
to real-world situations; and increased community engagement. For
the work reported here, an analytical chemistry laboratory was modified
to include a service-learning component with the goal of allowing
students to apply their newly acquired analytical skills to relevant,
real-world samples; to learn new analytical techniques; and to develop
professional communication skills. Students implemented a study of
the wastewater effluent at the Orlando Easterly Wetlands, an engineered
water polishing facility that removes nutrients from treated wastewater.
Students designed a sampling strategy, collected samples in the field,
and performed standard analysis on the water, including pH, chloride,
total dissolved solids, and phosphorus. Students also tested the water
for the artificial sweetener, sucralose, and characterized the concentration
throughout the flow path of the wetlands. Sucralose has been proposed
as an indicator of contamination of natural waters by anthropogenic
waste. This type of analysis has not been performed for this public
utility until now, and the students shared the results in a public
seminar. Student learning outcomes were compared to those in a conventional
section, with SL students showing comparable subject mastery and improved
self-efficacy.
We present a novel algorithm for baseline estimation in CE. The new algorithm which we have named as accelerated multiple-pass moving average (AMPMA) is combined to three preexisting low-pass filters, spike-removal, moving average, and multi-pass moving average filter, to achieve real-time baseline correction with commercial instrumentation. The successful performance of AMPMA is demonstrated with simulated and experimental data. Straightforward comparison of experimental data clearly shows the improvement AMPMA provides to the linear fitting, LOD, and accuracy (absolute error) of CE analysis.
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