A Simple Adsorption Experiment

Guirado, Gonzalo; Ayllón, José A.

doi:10.1021/ed100324s

Cited by 16 publications

(25 citation statements)

References 18 publications

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“…Despite the major advances in the built models, they (like any model does) have three major limitations in their applications: (1) We need to know the Abraham descriptors for a given chemical to predict its adsorption on one of the adsorbents; however, many emerging chemicals do not have those descriptors reported. Thus, future work should focus on obtaining the descriptors for these chemicals.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Aqueous adsorption, a long-standing purification/separation process, has been continuously investigated for decades; however, challenges still exist for this technology. Traditional batch and column experiments are time-consuming and inefficient toward a growing number of chemicals and adsorbents, and this has led to scarcity in data for adsorption of (new) compounds on (new) adsorbents. − Also, although the adsorption of many model chemicals like phenols on new or modified adsorbents has been explored extensively, − these abundant data have not been fully utilized other than for the comparison or selection of a small number of adsorbents. Mining published data to build broad predictive models will be a promising solution to fill the gaps.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning

Zhang

Zhong

Zhang

2020

Environ. Sci. Technol.

141

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Predictive models are useful tools for aqueous adsorption research; existing models such as multilinear regression (MLR), however, can only predict adsorption under specific equilibrium concentrations or for certain adsorption isotherm models. Also, few studies have discussed data processing beyond applying different modeling algorithms to improve the prediction accuracy. In this research, we employed a cosine similarity approach that focused on mining the available data before developing models; this approach can mine the most relevant data concerning the prediction target to build models and was found to considerably improve the prediction accuracy. We then built a machine-learning modeling process based on neural networks (NN), a group-selection data-splitting strategy for grouped adsorption data for adsorbent–adsorbate pairs under different equilibrium concentrations, and polyparameter linear free energy relationships (pp-LFERs) for aqueous adsorption of 165 organic compounds onto 50 biochars, 34 carbon nanotubes, 35 GACs, and 30 polymeric resins. The final NN-LFER models were successfully applied to various equilibrium concentrations regardless of the adsorption isotherm models and showed less prediction deviations than the published models with the root-mean-square errors 0.23–0.31 versus 0.23–0.97 log unit, and the predictions were improved by adding two key descriptors (BET surface area and pore volume) for the adsorbents. Finally, interpreting the NN-LFER models based on the Shapley values suggested that not considering equilibrium concentration and properties of the adsorbents in the existing MLR models is a possible reason for their higher prediction deviations.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, and Resins with Machine Learning

Zhang

Zhong

Zhang

2020

Environ. Sci. Technol.

141

View full text Add to dashboard Cite

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“…Red wine and fruit juices are very variable in their chemical composition, which is one of the possible reasons for the great variety of results the students obtained. Under normal conditions we would recommend to use well-defined pure dyes such as food-coloring dyes or other synthetic dyes as proposed by several authors. − …”

Section: The Home-lab Experimentsmentioning

confidence: 99%

“…In the lab we use filter paper as adsorbent, as recommended by other authors, but this was not available to our students. Our experiments showed that red wine adsorbed well on kitchen absorbent paper.…”

Section: The Home-lab Experimentsmentioning

confidence: 99%

“…Several adsorption isotherm experiments for student laboratories have been published − We implement that of Guirado et al, which comprises the following steps: (1) Prepare a stock solution of the dye and 10 diluted solutions with different dye concentrations. (2) Scan the absorption spectrum of the dye to determine the wavelength of maximal light absorbance and then measure the absorbance at this selected wavelength of each diluted solution.…”

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confidence: 99%

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When the Kitchen Turns into a Physical Chemistry Lab

2020

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We present our experience with transferring a four-day photometry and dye-adsorption laboratory experiment to the kitchens of students of Applied Thermodynamics from our degree in “Industrial Chemical Process Engineering”. The students designed and built a double-beam photometer using their smartphones and household materials, then prepared a series of dye-solutions with well-known relative concentrations and measured the absorbance–concentration calibration curve. After 24 h of adsorption to kitchen absorbent paper the solutions were visibly decolored. The students were able to determine the equilibrium dye concentrations from absorbance measurements and to calculate the adsorption isotherm. This home-lab experiment allowed them to keep on track with their lessons during the severe lock down in Spain due to COVID-19. They were highly motivated and achieved the learning objectives to a similar degree as in years before with conventional lab equipment.

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