Enhanced photocatalytic degradation of organic contaminants in water by highly tunable surface microlenses

Lu, Qiuyun; Yang, Lingling; Chelme-Ayala, Pamela; Li, Yanan; Zhang, Xuehua; El-Din, Mohamed Gamal

doi:10.1016/j.cej.2023.142345

Cited by 8 publications

(6 citation statements)

References 76 publications

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“…However, the enhancement factor by the concave MLA in photocatalytic degradation is relatively smaller than that in direct photodegradation (Figure 6L), since the

η

in the control group with ZnO is higher due to the contribution of photocatalysts. Similar phenomenon has been observed when applying convex MLAs in photocatalytic degradation of organic contaminants 60 …”

Section: Resultssupporting

confidence: 79%

“…Similar phenomenon has been observed when applying convex MLAs in photocatalytic degradation of organic contaminants. 60 Even though the enhancement factor by the concave MLA is not outstanding, the usage of concave MLA to enhance the photodegradation of organic contaminants is still promising. On one hand, concave MLAs are able to enhance the η of different organic pollutants.…”

Section: Photodegradation Efficiency Of Organic Contaminants With Con...mentioning

confidence: 99%

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Concave microlens arrays with tunable curvature for enhanced photodegradation of organic pollutants in water: A non‐contact approach

Lu,

Li,

Kassim

et al. 2023

EcoMat

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Solar‐driven photodegradation for water treatment faces challenges such as low energy conversion rates, high maintenance costs, and over‐sensitivity to the environment. In this study, we develop reusable concave microlens arrays (MLAs) for more efficient solar photodegradation by optimizing light distribution. Concave MLAs with the base radius of μm are fabricated by imprinting convex MLAs to polydimethylsiloxane elastomers. Concave MLAs possess a non‐contact reactor configuration, preventing MLAs from detaching or being contaminated. By precisely controlling the solvent exchange, concave MLAs are fabricated with well‐defined curvature and adjustable volume on femtoliter scale. The focusing effects of MLAs are examined, and good agreement is presented between experiments and simulations. The photodegradation efficiency of organic pollutants in water is significantly enhanced by 5.1‐fold, attributed to higher intensity at focal points of concave MLAs. Furthermore, enhanced photodegradation by concave MLAs is demonstrated under low light irradiation, applicable to real river water and highly turbid water.image

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“…However, the enhancement factor by the concave MLA in photocatalytic degradation is relatively smaller than that in direct photodegradation (Figure 6L), since the

η

Section: Resultssupporting

confidence: 79%

Section: Photodegradation Efficiency Of Organic Contaminants With Con...mentioning

confidence: 99%

Concave microlens arrays with tunable curvature for enhanced photodegradation of organic pollutants in water: A non‐contact approach

Lu,

Li,

Kassim

et al. 2023

EcoMat

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“…For R6G environmental media testing, unfiltered tap water was used for one set of samples. A second set of environmental samples was obtained by preparing synthetic river water as a media, 27 the composition of which is in Table S1.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Jayaprakash,

You,

Kanike

et al. 2024

Environ. Sci. Technol.

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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% crossvalidation 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.

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“…The curve of η with time for MO or SMX differs due to the variation of photodegradation mechanisms. [58] The η of MO under indoor solar light continuously increases because sufficient active species are generated under the visible light during the light treatment. Meanwhile, the photodegradation of SMX under irradiation in the visible range is restricted in the absence of photocatalysts, [59,60] so the η of SMX hardly increases after the irradiation of 50 days (d).…”

Section: Enhanced Photodegradation Under Indoor Solar Lightmentioning

confidence: 99%

“…[56,57] The advantages of solvent exchange include various available substrate materials, solution-based fabrication processes, and flexibility for adjusting the size and curvature of MLs. [58] However, surface MLs made by solvent-exchange process are still restricted on 1D fiber or 2D planar surfaces, which limits the development of surface ML-functionalized reactors for a broader range of applications in solar-water decontamination inside large reactors with curved surfaces.…”

Section: Introductionmentioning

confidence: 99%

Scalable and Facile Formation of Microlenses on Curved Surfaces Enabling a Highly Customized Sustainable Solar‐Water Nexus

Lu,

Khanna,

Chelme-Ayala

et al. 2023

Small Structures

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Solar‐driven water treatment suffers from low efficiency due to the solar energy loss during the energy conversion, especially in the scale‐up operation. One promising solution is using microlenses (MLs) to enhance the photodegradation of organic contaminants in water. However, most MLs fabrications apply to 2D planar surface only, which restricts their potential applications. In this study, a flexible and scalable technology is presented to fabricate MLs on curved surfaces. Precursor microdroplets form in a dilution process and are converted to MLs by photopolymerization. Optical simulations and experiments are combined to establish the correlation between optical properties of MLs and the performance of ML‐functionalized reactors in photodegradation. It is demonstrated that surface MLs on all‐shaped reactors significantly enhance the photodegradation efficiency of organic contaminants under simulated solar light or natural indoor light, with a maximum improvement of 83 folds. The surface coverage and size distribution of MLs can be adjusted by varying the solution concentration and the dilution rate when generating microdroplets. In addition, fabrication of MLs on a larger scale is achieved over an area up to 250 . MLs on 3‐dimensional curved surfaces fabricated by the technique enable significantly enhanced, highly customized, and sustainable solar‐driven water treatment.

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Enhanced photocatalytic degradation of organic contaminants in water by highly tunable surface microlenses

Cited by 8 publications

References 76 publications

Concave microlens arrays with tunable curvature for enhanced photodegradation of organic pollutants in water: A non‐contact approach

Concave microlens arrays with tunable curvature for enhanced photodegradation of organic pollutants in water: A non‐contact approach

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Scalable and Facile Formation of Microlenses on Curved Surfaces Enabling a Highly Customized Sustainable Solar‐Water Nexus

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