Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass.

The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit biomass derived activated carbon (AC). Many characterization methods utilized, confirmed the porous structure of synthesized AC. ANN and ANFIS models utilized pH, dose, initial TC concentration, mixing speed, time duration, and temperature as input parameters, whereas TC removal percentage was designated as the output parameter. The optimized configuration for the ANN model was determined as 6-8-1, while the ANFIS model employed trimf input and linear output membership functions. The obtained results showed a strong correlation, indicated by high R2 values (ANNR2: 0.9939 & ANFISR2: 0.9906) and low RMSE values (ANNRMSE: 0.0393 & ANFISRMSE: 0.0503). Apart from traditional isotherms, the dataset was fitted to statistical physics models wherein, the double-layer with a single energy satisfactorily explained the physisorption mechanism of TC adsorption. The sorption energy was 21.06 kJ/mol, and the number of TC moieties bound per site (n) was found to be 0.42, conclusive of parallel binding of TC molecules to the adsorbent surface. The adsorption capacity at saturation (Qsat) was estimated to be 466.86 mg/g - appreciably more than previously reported values. These findings collectively demonstrate that the AC derived from C. ramiflora fruit holds great potential for efficient removal of TC from a given system, and machine learning approaches can effectively model the adsorption processes.

Synthesis of magnetic biochar composite using Vateria indica fruits through in-situ one-pot hydro-carbonization for Fenton-like catalytic dye degradation.

The present study reports the synthesis, characterization, and application of sustainable magnetic biochar composite. The inedible fruits of Vateria indica, a powerful ayurvedic plant were hydrothermally transformed into magnetic biochar (BC-Fe3O4) in a single step and characterized by several sophisticated techniques. FESEM analysis portrayed fibrous irregular mesh-like biochar with surface clustered Fe3O4 nanoparticles, while the incidence of carbon, oxygen, and iron in the elemental analysis by EDS established magnetic biochar formation. Numerous peaks consistent with planes of (220), (311), (400), (422), (511), (440), and (120) also substantiated the occurrence of magnetite nanoparticles and biochar respectively, as analyzed by XRD. XPS analysis showed signals at 285.65 eV, 533.28 eV, 711.08 eV, and 724.68 eV corroborating a strong C-O bond, O1s orbit, Fe2+, and Fe3+ respectively. BC-Fe3O4 was superparamagnetic with saturation magnetization of 4.74 emu/g, as per VSM studies, while its specific surface area, pore volume, and pore diameter were 5.74 m2/g, 0.029 cm3/g, and 20.86 nm respectively. The Fenton-like degradation of methylene blue (5.0-25.0 ppm) was accomplished by synthesized BC-Fe3O4, in the presence of H2O2. Within 180 min, almost complete degradation was achieved, with first-order kinetics having rate constants between 0.0299 and 0.0167 min-1. Stability and recyclability studies performed over 7 cycles exhibited unaltered degradation between 93.98 and 97.59%. This study exhibits the exceptional characteristics and degradation capabilities of BC-Fe3O4 synthesized from a sustainable plant biomass.

Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass.

The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (-20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.

Enhanced adsorption of 2,4-dichlorophenoxyacetic acid using low-temperature carbonized Peltophorum pterocarpum pods and its statistical physics modeling.

The usage of various herbicides in the agricultural field leads to water pollution which is a big threat to the environment. Herein, the pods of the Peltophorum pterocarpum tree were used as a cheap resource to synthesize activated carbon (AC) by low-temperature carbonization to remove 2,4-dichlorophenoxyacetic acid (2,4-D) - an abundantly used herbicide. The exceptional surface area (1078.34 m2/g), mesoporous structure, and the various functional groups of the prepared AC adsorbed 2,4-D effectively. The maximum adsorption capacity was 255.12 mg/g, significantly higher than the existing AC adsorbents. The adsorption data satisfactorily modelled using Langmuir and pseudo-second-order models. Also, the adsorption mechanism was studied using a statistical physics model which substantiated the multi-molecular interaction of 2,4-D with the AC. The adsorption energy (<20 kJ/mol) and thermodynamic studies (ΔH°: -19.50 kJ/mol) revealed the physisorption and exothermicity. The practical application of the AC was successfully tested in various waterbodies by spiking experiments. Hence, this work confirms that the AC prepared from the pods of P. pterocarpum can be applied as a potential adsorbent to remove herbicides from polluted waterbodies.

Magnetic activated carbon synthesized using rubber fig tree leaves for adsorptive removal of tetracycline from aqueous solutions.

The current study emphasizes the activated carbon fabrication from rubber fig leaves, the establishment of its composite with iron oxide nanoparticles (RFAC@Fe2O3), and its relevance in the adsorptive elimination of tetracycline. The physical and functional properties of RFAC@Fe2O3 nanocomposite were uncovered by multiple approaches. Elemental analysis portrayed the existence of carbon, oxygen, and iron, while FESEM analysis revealed that Fe2O3 nanoparticle agglomerates were entrenched in the activated carbon matrix rendering it a rough abrasive texture. FT-IR analysis reported the presence of functional groups attributing to CC, -OH, crystalline iron oxide, and Fe-O stretching vibrations, and XRD corroborated graphitic crystalline structure, oxygenated functional groups attached to carbon accompanied by crystalline plane corresponding to Fe2O3 nanoparticles. XPS spectra depicted signature peaks for C, O, and Fe, while VSM studies designated its superparamagnetic nature. The high surface area (662.73 m2/g), pore size (3.12 nm), and mesoporous nature of RFAC@Fe2O3 make it apt for the adsorption of pollutants from contaminated samples. The adsorption of tetracycline (50 ppm) by RFAC@Fe2O3 was maximum at pH 4.0. As the nanocomposite dosage and stirring speed increased to 2.0 g/L and 150 rpm, maximum adsorption was observed due to more active binding sites and improved mixing. Freundlich isotherm along with pseudo-second-order model well described adsorption process divulging that tetracycline was adsorbed onto RFAC@Fe2O3 composite in multi-layers by chemisorption. Thermodynamic analysis signified negative values for ΔG°, while positive values for ΔH° and ΔS were obtained, indicating spontaneous feasible endothermic adsorption.

2,4-Dichlorophenoxyacetic acid (2,4-D) adsorptive removal by algal magnetic activated carbon nanocomposite.

In the present study, ferric oxide nanoparticles impregnated with activated carbon from Ulva prolifera biomass (UPAC-Fe2O3) were prepared and employed to remove 2,4-Dichlorophenoxyacetic acid (2,4-D) by adsorption. The UPAC-Fe2O3 nanocomposite was characterized for its structural and functional properties by a variety of techniques. The nanocomposite had a jagged, irregular surface with pores due to uneven scattering of Fe2O3 nanoparticles, whereas elemental analysis portrayed the incidence of carbon, oxygen, and iron. XRD analysis established the crystalline and amorphous planes corresponding to the iron oxide and carbon phase respectively. FT-IR analyzed the functional groups that confirmed the integration of Fe2O3 nanoparticles onto nanocomposite surfaces. VSM and XPS studies uncovered the superparamagnetic nature and presence of carbon and Fe2O3, respectively, in the UPAC-Fe2O3 nanocomposite. While the surface area was 292.51 m2/g, the size and volume of the pores were at 2.61 nm and 0.1906 cm3/g, respectively, indicating the mesoporous nature and suitability of the nanocomposites that could be used as adsorbents. Adsorptive removal of 2,4-D by nanocomposite for variations in process parameters like pH, dosage, agitation speed, adsorption time, and 2,4-D concentration was studied. The adsorption of 2,4-D by UPAC-Fe2O3 nanocomposite was monolayer chemisorption owing to Langmuir isotherm behavior along with a pseudo-second-order kinetic model. The maximum adsorption capacity and second order rate constant values were 60.61 mg/g and 0.0405 g/mg min respectively. Thermodynamic analysis revealed the spontaneous and feasible endothermic adsorption process. These findings confirm the suitability of the synthesized UPAC-Fe2O3 nanocomposite to be used as an adsorbent for toxic herbicide waste streams.

Adsorptive removal of tetracycline from aqueous solutions using magnetic Fe2O3 / activated carbon prepared from Cynometra ramiflora fruit waste.

Herein, the sustainable fabrication of magnetic iron oxide nanoadsorbent prepared with activated carbon of inedible Cynometra ramiflora fruit has been investigated. Activated carbon was obtained from phosphoric acid-treated C. ramiflora fruit, which was then utilized for the synthesis of magnetic nanocomposite (CRAC@Fe2O3). The formed nanocomposite was a porous irregular dense matrix of amorphous evenly sized spherical nanoparticles, as visualized by FESEM, and also contained carbon, oxygen, iron, and phosphorous in its elemental composition. FT-IR spectrum depicted characteristic bands attributing to Fe-O, C-OH, C-N, CC, and -OH bonds. VSM and XRD results proved that CRAC@Fe2O3 was superparamagnetic with a moderate degree of crystallinity and high saturation magnetization value (1.66 emu/g). Superior surface area, pore size, and pore volume of 766.75 m2/g, 2.11 nm, and 0.4050 cm3/g respectively were measured on BET analysis of CRAC@Fe2O3 nanocomposite, indicating their suitability for use as an adsorbent. On application of this nanocomposite for adsorption of tetracycline, maximum removal of 95.78% of 50 ppm TC at pH 4, CRAC@Fe2O3 0.4 g/L in 240 min. The adsorption of TC by CRAC@Fe2O3 was confirmed as monolayer sorption by ionic interaction (R2 = 0.9999) as it followed pseudo-second-order kinetics and Langmuir isotherm (R2 = 0.9801). CRAC@Fe2O3 showed a maximum adsorption capacity of 312.5 mg/g towards TC antibiotics indicating its potential for the treatment of antibiotic-contaminated samples. Since negative ΔGo and positive ΔHo and ΔSo values were obtained at all tested temperatures during the thermodynamic studies, the adsorption was confirmed to be endothermic, spontaneous, and feasible with an enhanced degree of randomness.