Copper biosorption over green silver nanocomposite using artificial intelligence and statistical physics formalism

Idrees, Fariha; Sibtain, Fakhra; Dar, M. Junaid; Shah, Fahad Hassan; Alam, Mahboob; Hussain, İqbal; Kim, Song Ja; Idrees, Jawaria; Khan, Shahid Ali; Salman, Saad

doi:10.1016/j.jclepro.2022.133991

Cited by 6 publications

(7 citation statements)

References 51 publications

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“…This model presumed that dexamethasone and hydrocortisone adsorption takes place through the creation of a single adsorbed layer with equal energy (Figure 2). In this case, each receptor site present in the adsorbent captures a variable number of molecules, characterized through the parameter n. The adsorbed amount versus the equilibrium concentration is expressed as follows [25]:…”

Section: Model 1: Single Layer (M1)mentioning

confidence: 99%

“…To investigate the adsorption phenomena, the energies ∆E 1 and ∆E 2 were determined for all systems at 25 • C. The calculated values of the adsorption energies summarized in Table 3 are lower than 20 kJ/mol, thus proving the presence of physical forces, which can take the form of Van der Waals interactions or hydrogen bindings. The Van der Waals forces are usually of the order of 10 kJ mol −1 , and the hydrogen bindings generally show values below 30 kJ mol −1 [25]. Therefore, the interactions between these two molecules can be estimated using the hydrogen binding interactions between hydroxyl group of the adsorbate molecule and the adsorbent receptor site.…”

Section: Analysis Of the Adsorption Energymentioning

confidence: 99%

“…where Z gc is the total grand canonical partition function [25], µ is the chemical potential of the receptor sites [25], β is defined as 1/k B T, T is the absolute temperature, and k B is the Boltzmann constant. Figure 6 represents the variation in internal energy versus the concentration for the two tested systems.…”

Section: Thermodynamic Interpretation 71 Internal Energymentioning

confidence: 99%

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Selective Extraction and Determination of Hydrocortisone and Dexamethasone in Skincare Cosmetics: Analytical Interpretation Using Statistical Physics Formalism

Aouaini,

Bouaziz,

Cherif

et al. 2024

Applied Sciences

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Molecularly imprinted polymers (MIPs), as magnetic extraction adsorbents, are used for the selective, rapid determination and extraction of dexamethasone and hydrocortisone in skincare products. Therefore, in this paper, magnetic molecularly imprinted polymers (MMIPs) and magnetic non-molecularly imprinted polymers (MNIPs) were utilized as adsorbents to describe the adsorption phenomena of dexamethasone and hydrocortisone. This interpretation, based on a statistical physics theory, applies the multilayer model with saturation to comprehend the adsorption of the drugs. Results obtained via numerical simulation revealed that dexamethasone and hydrocortisone adsorption happens via a non-parallel orientation on the surfaces of MMIPs and MNIPs, and they also showed that the adsorption amount of the MMIPs for the template molecule was notably greater than that of the MNIPs at the same initial concentration. The adsorption energy values retrieved from the data analysis ranged between 7.65 and 15.77 kJ/mol, indicating that the extraction and determination of dexamethasone and hydrocortisone is a physisorption process. Moreover, the distribution of a site’s energy was calculated to confirm the physical nature of the interactions between adsorbate/adsorbent and the heterogeneity of the surfaces of the MMIPs and MNIPs. Finally, the thermodynamic interpretation confirmed the exothermicity and spontaneous nature of the adsorption of these drugs on the tested adsorbents.

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Section: Model 1: Single Layer (M1)mentioning

confidence: 99%

Section: Analysis Of the Adsorption Energymentioning

confidence: 99%

Section: Thermodynamic Interpretation 71 Internal Energymentioning

confidence: 99%

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Selective Extraction and Determination of Hydrocortisone and Dexamethasone in Skincare Cosmetics: Analytical Interpretation Using Statistical Physics Formalism

Aouaini,

Bouaziz,

Cherif

et al. 2024

Applied Sciences

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“…7,8 The molecular mechanisms of adsorption can also be predicted through a well-known mathematical model known as statistical physics formalism (SPF). 9 One of the key concepts in SPF is the partition function (PF), which describes the probability distribution of molecules in each system. The PF is used in adsorption research to determine a system's equilibrium adsorption parameters, including the heat of adsorption and isotherms.…”

Section: Introductionmentioning

confidence: 99%

“…From H 2 SO 4 impregnation 32 to steam activation, phosphoric acid treatment to AgNPs impregnation, an array of modification techniques have been explored to augment adsorption capacity. A diverse range of natural materials as adsorbents, including hazelnut shells, 32 hulls of Ceiba pentandra , 33 Typha latifolia L., 34 pomelo peels, 35 green vegetable waste, 36 pistachio shells, apricot stone, almond, walnut, 37 grape bagasse, 38 hazelnut husk, 39 lignocellulose, 9 pecan shells, 40 rubber wood sawdust, 41 pinewood sawdust, 42 peanut shells, 43 apricot stone, 44 chestnut shells, and grape seeds 45 were previously utilized. The reported uptake percentages and capacities vary across these studies, with some adsorbents demonstrating high efficiency (>80%) while others exhibit moderate to substantial capacities.…”

Section: Introductionmentioning

confidence: 99%

Ion-Exchange Resin/Carrageenan-Copper-Based Nanocomposite: Artificial Neural Network, Advanced Thermodynamic Profiling, and Anticoagulant Studies

Haider,

Ullah,

Kazi

et al. 2024

ACS Omega

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Carrageenan (CG) and ion exchange resins (IERs) are better metal chelators. Kappa (κ) CG and IERs were synthesized and subjected to copper ion (Cu 2+ ) adsorption to obtain DMSCH/κ-Cu, DC20H/κ-Cu, and IRP69H/κ-Cu nanocomposites (NCs). The NCs were studied using statistical physics formalism (SPF) at 315−375 K and a multilayer perceptron with five input nodes. The percentage of Cu 2+ uptake efficiency was used as an outcome variable. Via the grand canonical ensemble, SPF gives models for both monolayer and multilayer sorption layers. For in vitro anticoagulant activity (ACA), the activated partial thromboplastin time were calculated using 100 μL of rabbit plasma incubated at 37 °C. After 2 min, 100 L of 0.025 M CaCl 2 was added, and the clotting time was recorded for each group (n = 6). The results demonstrated that the key covariables for the adsorption process were pH and concentration. The results of artificial neural network models were comparable with the experimental findings. The error rates varied between 4.3 and 1.0%. The prediction analysis results ranged from 43.6 to 89.2. The ΔG and ΔS values for IRP69H/κ-Cu obtained were −18.91 and −16.32 and 26.21 and 22.74 kJ/mol for the temperatures 315 and 345 K, respectively. Adsorbate species were perpendicular to the adsorbent surfaces, notwithstanding the apparent importance of macro-and micropore volumes. These adsorbents typically fluctuate with temperature changes and contain one or more layers of sorption. Negative and positive sorption energies correspond to endothermic and exothermic processes. The biosorption energy (E1 and E2) values in this experiment have a value of less than 23 kJ mol −1 . Complex SPF models' energy distributions validate surface properties and interactions with adsorbates. At a concentration of 100 μg/mL, DC20H/κ-Cu 2+ exhibited an ACA of only 8 s. These NCs demonstrated better greater ACA with the order DC20H/κ < DMSCH/κ < IRP69H/κ. More research is needed to rule out the chemical processes behind the ACA of CG/IER-Cu NCs.

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Application of artificial neural networks and Langmuir and Freundlich isotherm models to the removal of textile dye using biosorbents: A comparative study among methodologies

Petroli,

Brocardo de Leon,

Di Domenico

et al. 2024

Can J Chem Eng

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Adsorption isotherms are valuable tools for describing the interaction between adsorbate and adsorbent since they demonstrate the equilibrium relationship. The Langmuir and Freundlich models are the most commonly used isotherm models to describe these relationships; still, they cannot consistently deliver efficient results due to the assumptions of the model not predicting more complex situations as occurs in biosorption. Artificial neural networks (ANN) are a set of algorithms modelled loosely after the human brain and are designed to recognize patterns. The ANN tool can overcome problems isotherm models have in describing the interactions mentioned and help define the best conditions for a given adsorption process. This paper reports the application of ANNs for predicting the removal efficiency of textile dye Neolan Black WA (Acid Black 52) using orange peel and sugarcane bagasse as biosorbents. The Freundlich, Langmuir, pseudo‐first‐order, and pseudo‐second‐order models were applied and compared to the ANN model. The parameters evaluated were initial dye concentration (10–600 mg/L), final dye concentration (0–83.44 mg/L), biosorbent mass (1.5 g), pH (2), and contact time of dye (0.167–24 h). Two classes of ANNs, Elman and feed‐forward networks, were tested with a mean square error of 0.0212 and 0.7274 for the isotherm and kinetics, respectively. Compared to the conventional isotherm and kinetic models, the Elman network predicted the amount adsorbed by the biosorbents with higher precision, acquiring a determination coefficient of 0.9998 and a mean square error of 8.75 × 10−5.

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Copper biosorption over green silver nanocomposite using artificial intelligence and statistical physics formalism

Cited by 6 publications

References 51 publications

Selective Extraction and Determination of Hydrocortisone and Dexamethasone in Skincare Cosmetics: Analytical Interpretation Using Statistical Physics Formalism

Selective Extraction and Determination of Hydrocortisone and Dexamethasone in Skincare Cosmetics: Analytical Interpretation Using Statistical Physics Formalism

Ion-Exchange Resin/Carrageenan-Copper-Based Nanocomposite: Artificial Neural Network, Advanced Thermodynamic Profiling, and Anticoagulant Studies

Application of artificial neural networks and Langmuir and Freundlich isotherm models to the removal of textile dye using biosorbents: A comparative study among methodologies

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