Machine Learning-Assisted Volatile Organic Compound Gas Classification Based on Polarized Mixed-Potential Gas Sensors

Wang, Bin; Zhang, Jianyu; Wang, Tong; Li, Weijia; Lu, Qi; Sun, Huaiyuan; Huang, Lingchu; Liang, Xishuang; Liu, Fengmin; Liu, Fangmeng; Sun, Peng; Lu, Geyu

doi:10.1021/acsami.2c17348

Cited by 11 publications

(10 citation statements)

References 53 publications

(76 reference statements)

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“…[24] Figure 3c presents the peaks of Co 2p located at 782.1 and 797.1 eV, corresponding to 2p 3/2 and 2p 1/2 of Co 3+ , [30] respectively. Figure 3d displays high-resolution spectra of Ag 3d, there is a set of peaks located at 368.0 and 374.1 eV corresponding to 3d 5/2 and 3d 3/2 of Ag 0 , [31] respectively. Meanwhile, the peaks of binding energy position at 367.2 and 373.2 eV are attributed to 3d 5/2 and 3d 3/2 of Ag + .…”

Section: Resultsmentioning

confidence: 99%

Reinforcement of Electrocatalytic Oxygen Evolution Activity Enabled by Constructing Silver‐Incorporated NiCo‐PBA@NiFe‐LDH Hierarchical Nanoboxes

Cao,

Wu,

Zhou

et al. 2023

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Complicated oxygen evolution reaction (OER) poses the bottleneck in improving the efficiency of hydrogen production through water electrolysis. Herein, an integrated strategy to modulate the electronic structure of NiFe layered double hydroxide (NiFe‐LDH) is reported by constructing Ag‐incorporated NiCo‐PBA@NiFe‐LDH heterojunction with a hierarchical hollow structure. This “double heterojunction” facilitates local charge polarization at the interface, thereby promoting electron transfer and reducing the adsorption energy of intermediates, ultimately enhancing the intrinsic activity of the catalyst. It is noteworthy that an exchange bias field is observed between NiCo‐PBA and NiFe‐LDH, which will be conducive to regulating the electron spin states of metals and facilitating the production of triplet oxygen. Additionally, the unique hierarchical nanoboxes provide a large specific surface area that ensures adequate exposure to adsorption sites and active sites. Profiting from the synergistic advantages, the overpotential is as low as 190 mV at a current density of 10 mA cm−2, with a low Tafel slope of 21 mV dec−1. Moreover, density functional theory (DFT) calculation further substantiated that the incorporation of Ag in the heterojunction can effectively reduce the adsorption energy of reactant intermediates and enhance the conductivity.

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Section: Resultsmentioning

confidence: 99%

Reinforcement of Electrocatalytic Oxygen Evolution Activity Enabled by Constructing Silver‐Incorporated NiCo‐PBA@NiFe‐LDH Hierarchical Nanoboxes

Cao,

Wu,

Zhou

et al. 2023

Small

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“…Huang Y. et al [125] were successful in fabricating pHresponsive nanopesticides from a fungicide carbendazim, trimethylammoniumpillar(5)arene, methyl orange-functionalized mesoporous selenium. It was observed that the cumulative release of carbendazim was increased by 1.74 times at a pH of 4.5 compared to the general environmental pH.…”

Section: Green Nanopesticidesmentioning

confidence: 99%

Nanopesticides: A Review on Current Research and Future Perspective

Paradva,

Kalla

2023

ChemistrySelect

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The survival of humans depends critically on crop production, making crop protection absolutely essential. Numerous studies have suggested that nanopesticides could soon fully replace conventional pesticides since they are far more effective than those now in use. Due to an increase in their surface‐to‐volume ratio, nanomaterials exhibit different properties from their bulk counterparts. Nanopesticides are pesticides that have been made with these nanoparticles and have a high application efficiency and significantly fewer hazardous environmental impacts than pesticides that have been made chemically. Various formulation strategies, which include the containment of the active substance by the nanoparticles, have previously been established, exhibiting promising effects on the pests. Target specificity, high stability, and controlled active ingredients (AIs) release are a few of the noteworthy qualities these nanopesticides offer. The development of green nanopesticides derived from nature, which have very low ecotoxicities and nanoscale qualities, can also have a significant positive impact on crop output. However, before being mass‐produced and used, the discovery still has to be thoroughly examined to determine its effects on people and the environment due to the lack of conclusive proof demonstrating its toxicity. This paper aims to represent the use of nanopesticides in crop protection and a detailed overview of the various methods for the formulation of nanopesticides. Also, the possible negative impacts on the non‐target species by the application of nanopesticides have been briefly discussed. The need for the development of green nanopesticides to avoid the negative aspects of nanopesticides has also been highlighted.

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“…As far as we are concerned, these techniques are not yet applied to classify sources of turbidity. Examples of the use of ML combined with sensors to identify the origin of a given substance are well reported in the use of gas sensors [23,24].…”

Section: Introductionmentioning

confidence: 99%

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra,

Ahmad,

Sendra

et al. 2024

Chemosensors

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Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.

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Machine Learning-Assisted Volatile Organic Compound Gas Classification Based on Polarized Mixed-Potential Gas Sensors

Cited by 11 publications

References 53 publications

Reinforcement of Electrocatalytic Oxygen Evolution Activity Enabled by Constructing Silver‐Incorporated NiCo‐PBA@NiFe‐LDH Hierarchical Nanoboxes

Reinforcement of Electrocatalytic Oxygen Evolution Activity Enabled by Constructing Silver‐Incorporated NiCo‐PBA@NiFe‐LDH Hierarchical Nanoboxes

Nanopesticides: A Review on Current Research and Future Perspective

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

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