Features of Electrical Double Layers Formed Around Strongly Charged Nanoparticles Immersed in an Electrolyte Solution. The Effect of Ion Sizes

Dolinnyi, A. I.

doi:10.1134/s1061933x19060048

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…The specific features (sizes, hydration, charge location, and many more) of counterions and macroions are important in regulating their interactions [6] . Meanwhile, there are very few techniques that can make direct and accurate measurements of counterions, especially for the counterions only loosely associated in the diffuse layers, which are important for the Debye length [4a, 7] …”

Section: Figurementioning

confidence: 99%

Accurate Determination of the Quantity and Spatial Distribution of Counterions around a Spherical Macroion

Chen

Bera

et al. 2021

Angew Chem Int Ed

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The accurate distribution of countercations (Rb+ and Sr2+) around a rigid, spherical, 2.9‐nm size polyoxometalate cluster, {Mo132}42−, is determined by anomalous small‐angle X‐ray scattering. Both Rb+ and Sr2+ ions lead to shorter diffuse lengths for {Mo132} than prediction. Most Rb+ ions are closely associated with {Mo132} by staying near the skeleton of {Mo132} or in the Stern layer, whereas more Sr2+ ions loosely associate with {Mo132} in the diffuse layer. The stronger affinity of Rb+ ions towards {Mo132} than that of Sr2+ ions explains the anomalous lower critical coagulation concentration of {Mo132} with Rb+ compared to Sr2+. The anomalous behavior of {Mo132} can be attributed to majority of negative charges being located at the inner surface of its cavity. The longer anion–cation distance weakens the Coulomb interaction, making the enthalpy change owing to the breakage of hydration layers of cations more important in regulating the counterion–{Mo132} interaction.

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Section: Figurementioning

confidence: 99%

Accurate Determination of the Quantity and Spatial Distribution of Counterions around a Spherical Macroion

Chen

Bera

et al. 2021

Angew Chem Int Ed

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“…Water flow moved upwards along charged nanochannels through the capillary action, when water evaporation occurred. , The positively charged hydronium ions were transported to the downstream area with a continuous water flow and generated a positive electromotive voltage. The negatively charged SiNWs contacted with the positively charged hydrogen ions to form an electric double-layer structure. − Owing to the presence of the dielectric material of SiNWs, the flowing hydrogen ions relied on the Coulomb force to induce the electric charge of directional migration in the SiNWs, and a gradient concentration of electrons generated. Then, the potential difference was formed along with accumulation of electrons, resulting in the streaming potential. , Finally, the constant direct current electricity output was achieved assisted with fabric electrodes and silver electrodes in SiNWs/SISCF-based hydrovoltaic devices.…”

Section: Resultsmentioning

confidence: 99%

All-Weather Freshwater and Electricity Simultaneous Generation by Coupled Solar Energy and Convection

Fang

Liu

Chen

et al. 2022

ACS Appl. Mater. Interfaces

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Integrating solar evaporation-driven desalination and electricity production has emerged as a promising approach to alleviate energy crisis and freshwater scarcity. However, there remain huge challenges to achieve high water productivity and steady power generation efficiency. Herein, a compact evaporation-induced water–electricity co-generation device was proposed using a bio-waste squid ink sphere-based cellulose fabric as an evaporator and a silicon nanowires array-based evaporation-driven moist-electric generator. The efficient localized solar thermal heating of the photothermal component leads to significant enhancement in freshwater yield, and the latent heat of vapor condensation is recycled to promote the electricity generation. More notably, the device is capable of harvesting wind energy toward all-weather water and power generation. The fabricated device demonstrated a high evaporation rate of 2.17 kg m–2 h–1 with a collection rate of 66.7% and a maximum output voltage of 1.48 V under one sun illumination with a wind speed of 4 m s–1. The outdoor experiments display a maximum water evaporation rate of 1.84 kg m–2 h–1 with a maximum output voltage of 1.35 V even on cloudy days. Such superior performance of a comprehensive device has great potential for sustainable and practical application in freshwater and electricity generation.

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“…It is a thin, tightly packed layer of ions that is present just adjacent to the surface of a charged particle. Since there is a high electrostatic attractive force present, the ions in this layer remain immobile [ 44 , 45 ]. On the other hand, beyond this layer, the ions in the solution are free to move about.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

et al. 2023

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Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models’ predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand–brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields.

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Features of Electrical Double Layers Formed Around Strongly Charged Nanoparticles Immersed in an Electrolyte Solution. The Effect of Ion Sizes

Cited by 9 publications

References 29 publications

Accurate Determination of the Quantity and Spatial Distribution of Counterions around a Spherical Macroion

Accurate Determination of the Quantity and Spatial Distribution of Counterions around a Spherical Macroion

All-Weather Freshwater and Electricity Simultaneous Generation by Coupled Solar Energy and Convection

Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning

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