Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm
Abstract
:1. Introduction
2. Results and Discussion
2.1. Physicochemical Characterization
2.2. Characterization
2.2.1. Zero-Point Charge
2.2.2. Scanning Electron Microscopy
2.2.3. FTIR Analysis
2.3. Biosorption Results
2.3.1. Effect of Solution pH
2.3.2. Adsorbent Mass Effect
2.3.3. Effect of Contact Time
2.3.4. Temperature Effect on Sorption Capacity
2.4. Adsorption and Desorption Study
2.5. Comparison of Natural and Alkali Ardisia Compressa K. with Other Biosorbents
2.6. Proposed Mechanism of Interaction of Pb2+ with Raw-AC and AC-OH
2.7. Data-Driven Optimization
- In the first step: the original data was arranged in the form of a database (see Table 11), where all the tuning variables and the performance indicators were placed together. The database contained a total of 178 experimental points. Bringing the data in such a format would help in the development of an empirical model [61]. It was necessary to develop an empirical model because the optimization algorithms required a direct correlation between the tuning variables and the performance indicator. Thus, it was also necessary to understand the data through the heatmap of the Pearson coefficient of correlation (see Table 12) [62]. It was noted that the initial concentration of Pb was the variable with the highest level of sensitivity for qe, and it was also noted that material 2 had a relatively higher tendency for higher values of qe. The temperature and the pH of the solution were the least sensitive.
- 2.
- In the second step, an empirical model was developed for the selected database. Such a model was developed through the application of artificial neural networks for regression models. The MATLAB environment using the nftool box was implemented. All the input variables were placed in the input layer, and the performance indicator was placed in the performance indicator. A backpropagation method called Bayesian regularization backpropagation was implemented, which was suitable for the noisy database as the origin was experimentation [63]. The training, testing, and validation percentages were set to be 70, 15, and 15, respectively. The number of hidden neurons was iterated and a total of eight neurons came out to be optimal for the minimum mean square error. The regression fit and the error histogram are displayed in Figure 11. During the training, testing, and total phase, the values of R came out to be 0.99276, 0.977, and 0.9905, respectively, which indicates a high goodness of fit. The error histogram also fulfills the normality assumption of errors.
- 3.
- Once the empirical model was developed, in the last step, an optimization study could be conducted. The optimization study was conducted to maximize qe for the given range of each of the tuning variables. The genetic algorithm was implemented in the MATLAB environment through its Optimization toolbox. All the generic configuration of the algorithm was taken to be default [64]. The optimization was conducted two times, one for each material, and the results are shown in Table 13. It is noted that the combination of tuning variables for each type of material was quite similar. For example, for Raw-AC, the optimal combination of temperature, pH of the solution, and contact time came out to be the same, which was 298.15 K, 6, and 1440 min, respectively. The only difference was noted in the cases of initial concentration of Pb and mass, which were 854.16 mg/L and 0.1 g for Raw-AC and 1012.98 mg/L and 0.05 g for AC-OH, respectively. It is quite interesting that even though a couple of variables were the same, the value of optimal qe was quite different. For the combination of Raw-AC and AC-OH, the optimal qe values came out to be 62.287 mg/g and 147.475 mg/g, respectively. It is remarkable how doing such a treatment can improve the results by 57.76%. It is also to be stressed here that the optimal point evaluated through the computational method was placed back into the experimental conditions and conformity between the optimal point through the computational method and the experimentation was met.
3. Materials and Methods
3.1. Pb standard Solutions
3.2. Biosorbents
3.3. Physicochemical Analysis
3.4. Characterization Techniques
3.5. Biosorption Study
3.6. Kinetic Models
3.7. Isotherm Models
3.8. Adsorption–Desorption Cycles
3.9. Framework of Empirical Model and Optimization Process
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
References
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Parameter | Percent Composition (% w/w) |
---|---|
Total soluble solids (%) | 86.49 ± 0.12 |
Moisture (%) | 13.51 ± 0.12 |
Ash (%) | 6.74 ± 0.01 |
Ethereal extract (%) Raw fiber (%) Protein (%) | 3.60 ± 0.29 16.51 ± 0.34 5.83 ± 0.12 |
Nitrogen-free extract (%) | 53.80 ± 0.36 |
Biosorbent | pHPZC (Raw Biosorbent) | pHPZC (Alkali-Treated Biosorbent) | Ref. |
---|---|---|---|
Prunus armeniaca L. shells | 4.9 | 5.7 | [31] |
Leucaena leucephala leaves | 6.7 | 7.2 | [32] |
Cupressus sempervirens Carob shells Nostoc commune | 6.1 5.4 1.3 | 6.7 6.52 2.5 | [33] [34] [28] |
Ardisia compressa K. | 4.8 | 6.4 | This study |
pH | Model | Parameters | Raw-AC | AC-OH |
---|---|---|---|---|
2 | Langmuir | Qmax (mg/g) | 26.4 | 43.7 |
KL·10−2 (L/mg) | 2.001 | 59.431 | ||
R2 | 0.981 | 0.971 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 3.580 | 16.226 | |
n | 3.200 | 5.404 | ||
R2 | 0.960 | 0.924 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 21.948 | 41.965 | |
β (mol2/kJ2) | 93.593 | 0.242 | ||
E (kJ/mol) | 0.073 | 1.437 | ||
R2 | 0.897 | 0.917 | ||
4 | Langmuir | Qmax (mg/g) | 48.1 | 69.41 |
KL·10−2 (L/mg) | 0.882 | 56.634 | ||
R2 | 0.992 | 0.984 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 2.229 | 23.975 | |
n | 2.084 | 4.979 | ||
R2 | 0.970 | 0.913 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 34.183 | 62.050 | |
β (mol2/kJ2) | 413.006 | 0.100 | ||
E (kJ/mol) | 0.035 | 2.236 | ||
R2 | 0.932 | 0.867 | ||
6 | Langmuir | Qmax (mg/g) | 96.4 | 171.0 |
KL·10−2 (L/mg) | 0.781 | 9.076 | ||
R2 | 0.997 | 0.990 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 2.654 | 22.311 | |
n | 1.710 | 2.043 | ||
R2 | 0.992 | 0.976 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 55.216 | 120.205 | |
β (mol2/kJ2) | 239.383 | 2.729 | ||
E (kJ/mol) | 0.046 | 0.428 | ||
R2 | 0.910 | 0.913 |
Biosorbent | pH | Model | Error Functions | |||||
---|---|---|---|---|---|---|---|---|
APE | SSE | ∆q(%) | χ2 | EABS | RMSE | |||
Raw-AC | 2 | Langmuir | 9.934 | 4.703 | 19.201 | 1.790 | 5.898 | 0.970 |
Freundlich | 16.261 | 21.035 | 19.881 | 1.581 | 10.604 | 2.051 | ||
D-R | 36.483 | 51.896 | 57.739 | 353.992 | 15.899 | 3.222 | ||
4 | Langmuir | 12.051 | 8.054 | 17.513 | 1.223 | 5.875 | 1.269 | |
Freundlich | 19.734 | 36.164 | 23.840 | 2.180 | 13.922 | 2.689 | ||
D-R | 47.706 | 81.307 | 70.448 | 509.599 | 21.196 | 4.033 | ||
6 | Langmuir | 16.786 | 9.119 | 30.325 | 3.133 | 6.721 | 1.510 | |
Freundlich | 17.545 | 25.427 | 29.171 | 1.516 | 12.695 | 2.255 | ||
D-R | 53.582 | 272.070 | 72.248 | 903.793 | 41.273 | 7.377 | ||
AC-OH | 2 | Langmuir | 6.402 | 54.695 | 8.453 | 3.009 | 14.617 | 3.307 |
Freundlich | 17.359 | 142.978 | 24.869 | 7.253 | 28.274 | 5.347 | ||
D-R | 30.443 | 156.436 | 47.782 | 911.672 | 30.073 | 5.593 | ||
4 | Langmuir | 24.359 | 87.296 | 30.821 | 10.184 | 19.823 | 4.178 | |
Freundlich | 68.640 | 469.134 | 43.902 | 16.675 | 52.871 | 9.686 | ||
D-R | 38.773 | 719.036 | 53.671 | 51.392 | 54.909 | 11.992 | ||
6 | Langmuir | 16.701 | 169.569 | 21.784 | 4.109 | 25.363 | 5.824 | |
Freundlich | 81.765 | 402.405 | 131.184 | 16.084 | 48.817 | 8.971 | ||
D-R | 55.164 | 1427.996 | 71.002 | 427.180 | 88.157 | 16.900 |
Dose (g/L) | Model | Parameters | Raw-AC | AC-OH |
---|---|---|---|---|
5 | Langmuir | Qmax (mg/g) | 53.7 | 73.7 |
KL·10−2 (L/mg) | 2.105 | 0.773 | ||
R2 | 0.996 | 0.957 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 4.905 | 23.916 | |
n | 2.494 | 4.414 | ||
R2 | 0.953 | 0.953 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 34.771 | 87.074 | |
β (mol2/kJ2) | 68.942 | 81.735 | ||
E (kJ/mol) | 0.085 | 0.078 | ||
R2 | 0.908 | 0.836 | ||
15 | Langmuir | Qmax (mg/g) | 53.5 | 53.3 |
KL·10−2 (L/mg) | 0.613 | 128.915 | ||
R2 | 0.996 | 0.966 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 0.952 | 24.446 | |
n | 1.539 | 2.829 | ||
R2 | 0.986 | 0.964 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 29.303 | 47.476 | |
β (mol2/kJ2) | 310.592 | 0.136 | ||
E (kJ/mol) | 0.040 | 1.916 | ||
R2 | 0.914 | 0.905 | ||
20 | Langmuir | Qmax (mg/g) | 50.1 | 90.6 |
KL·10−2 (L/mg) | 0.801 | 30.554 | ||
R2 | 0.998 | 0.993 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 0.941 | 20.674 | |
n | 1.472 | 1.395 | ||
R2 | 0.989 | 0.997 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 25.706 | 42.859 | |
β (mol2/kJ2) | 157.144 | 0.212 | ||
E (kJ/mol) | 0.056 | 1.537 | ||
R2 | 0.955 | 0.951 |
Biosorbent | Ci (mg/L) | Pseudo-First-Order | Pseudo-Second-Order | ||||
---|---|---|---|---|---|---|---|
qe,cal (mg/g) | k1 (1/min) | R2 | qe,cal (mg/g) | k2 (g/mg min) | R2 | ||
Raw-AC | 25 | 2.4 | 0.341 | 0.976 | 2.4 | 0.970 | 0.994 |
50 | 6.0 | 0.118 | 0.985 | 6.2 | 0.038 | 0.987 | |
100 | 11.7 | 0.228 | 0.941 | 11.9 | 0.058 | 0.973 | |
250 | 16.7 | 0.332 | 0.990 | 16.7 | 0.148 | 0.991 | |
AC-OH | 25 | 3.2 | 0.409 | 0.999 | 3.2 | 3.175 | 0.999 |
50 | 6.7 | 0.298 | 0.999 | 6.8 | 0.728 | 0.999 | |
100 | 13.6 | 0.232 | 0.999 | 13.7 | 0.153 | 0.999 | |
250 | 34.5 | 0.200 | 0.997 | 34.8 | 0.032 | 1.000 |
Biosorbent | Ci (mg/L) | Model | Error Functions | |||||
---|---|---|---|---|---|---|---|---|
ARE | SSE (10−2) | ∆q(%) (10−2) | χ2 (10−2) | EABS | RMSE | |||
Raw-AC | 25 | PFO | 1.486 | 1.531 | 2.099 | 0.635 | 0.251 | 0.055 |
PSO | 1.285 | 1.065 | 1.728 | 0.444 | 0.218 | 0.046 | ||
50 | PFO | 1.758 | 9.475 | 2.218 | 1.633 | 0.698 | 0.138 | |
PSO | 1.725 | 8.188 | 2.166 | 1.510 | 0.663 | 0.128 | ||
100 250 | PFO | 2.790 | 99.482 | 3.577 | 8.505 | 2.252 | 0.446 | |
PSO | 1.712 | 44.854 | 2.446 | 3.874 | 1.360 | 0.300 | ||
PFO | 1.051 | 28.115 | 1.297 | 1.688 | 1.226 | 0.237 | ||
PSO | 0.933 | 26.359 | 1.253 | 1.583 | 1.089 | 0.230 | ||
AC-OH | 25 | PFO | 0.310 | 0. 093 | 0.424 | 0.029 | 0.060 | 0.015 |
PSO | 0.298 | 0. 064 | 0.352 | 0.019 | 0.057 | 0.013 | ||
50 | PFO | 0.318 | 0.552 | 0.495 | 0.082 | 0.128 | 0.037 | |
PSO | 0.295 | 0.459 | 0.452 | 0. 068 | 0.119 | 0.034 | ||
100 250 | PFO | 0.136 | 0.446 | 0.220 | 0.033 | 0.111 | 0.033 | |
PSO | 0.341 | 1.560 | 0.414 | 0.116 | 0.276 | 0.062 | ||
PFO | 0.711 | 46.737 | 0.889 | 1.355 | 1.467 | 0.342 | ||
PSO | 0.263 | 6.943 | 0.345 | 0.204 | 0.539 | 0.132 |
Temperature (K) | Model | Parameters | Raw-AC | AC-OH |
---|---|---|---|---|
300.15 | Langmuir | Qmax (mg/g) | 96.4 | 171 |
KL·10−2 (L/mg) | 0.781 | 9.076 | ||
R2 | 0.997 | 0.990 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 2.654 | 22.311 | |
n | 1.71 | 2.043 | ||
R2 | 0.992 | 0.976 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 53.840 | 120.205 | |
β (mol2/kJ2) | 227.720 | 2.729 | ||
E (kJ/mol) | 0.047 | 0.428 | ||
R2 | 0.899 | 0.913 | ||
313.15 | Langmuir | Qmax (mg/g) | 76.1 | 49.6 |
KL·10−2 (L/mg) | 0.417 | 42.48 | ||
R2 | 0.988 | 0.971 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 1.375 | 14.096 | |
n | 1.671 | 4.173 | ||
R2 | 0.999 | 0.884 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 38.081 | 47.671 | |
β (mol2/kJ2) | 264.046 | 0.324 | ||
E (kJ/mol) | 0.044 | 1.243 | ||
R2 | 0.865 | 0.970 | ||
330.15 | Langmuir | Qmax (mg/g) | 92.4 | 45.3 |
KL·10−2 (L/mg) | 0.231 | 24.844 | ||
R2 | 0.981 | 0.959 | ||
Freundlich | KF (mg/g)(L/mg)1/n | 0.508 | 12.635 | |
n | 1.311 | 3.329 | ||
R2 | 0.985 | 0.904 | ||
Dubinin–Radushkevich (D-R) | Qmax (mg/g) | 30.879 | 39.923 | |
β (mol2/kJ2) | 494.126 | 0.503 | ||
E (kJ/mol) | 0.032 | 0.997 | ||
R2 | 0.904 | 0.887 |
Biosorbent | T (K) | Model | Error Functions | |||||
---|---|---|---|---|---|---|---|---|
APE | SSE | ∆q(%) | χ2 | EABS | RMSE | |||
Raw-AC | 300.15 | Langmuir | 19.583 | 9.119 | 0.303 | 3.133 | 6.721 | 1.350 |
Freundlich | 20.027 | 20.996 | 0.291 | 1.405 | 11.534 | 2.049 | ||
D-R | 52.451 | 298.240 | 70.076 | 608.200 | 42.590 | 7.723 | ||
313.15 | Langmuir | 26.940 | 20.969 | 0.397 | 7.404 | 11.441 | 2.048 | |
Freundlich | 5.468 | 1.262 | 0.114 | 0.211 | 2.160 | 0.502 | ||
D-R | 52.210 | 244.613 | 67.720 | 210.523 | 35.096 | 6.994 | ||
330.15 | Langmuir | 23.035 | 16.390 | 0.305 | 3.404 | 9.584 | 1.811 | |
Freundlich | 18.858 | 12.375 | 0.242 | 1.310 | 7.947 | 1.573 | ||
D-R | 47.879 | 75.587 | 63.686 | 94.235 | 19.872 | 3.888 | ||
AC-OH | 300.15 | Langmuir | 16.701 | 169.569 | 0.218 | 4.109 | 25.363 | 5.824 |
Freundlich | 81.765 | 402.405 | 1.312 | 16.084 | 48.817 | 8.971 | ||
D-R | 55.163 | 1427.996 | 71.002 | 427.158 | 88.157 | 16.900 | ||
313.15 | Langmuir | 54.847 | 80.379 | 1.131 | 7.002 | 20.212 | 4.009 | |
Freundlich | 102.467 | 317.822 | 2.177 | 19.917 | 40.802 | 7.973 | ||
D-R | 37.865 | 97.503 | 57.454 | 78.557 | 23.568 | 4.416 | ||
330.15 | Langmuir | 18.713 | 84.480 | 0.244 | 3.088 | 20.839 | 4.110 | |
Freundlich | 71.759 | 196.337 | 1.261 | 13.284 | 35.860 | 6.266 | ||
D-R | 43.927 | 231.236 | 59.709 | 10.934 | 35.336 | 6.801 |
Biosorbent | Dose (g/L) | Ci (mg/L) | pH | Kinetic Model | Mechanism | qe,max (mg·g−1) | Ref. |
---|---|---|---|---|---|---|---|
Moringa oleifera tree leaves | 4 | 20–200 | 5 | PSO | Chemisorption | 34.6 | [42] |
Nostoc commune | 0.5 | 127- 442 | 4.5–5.5 | PSO | Chemisorption | 384.6 | [28] |
Cystoseira stricta | 1 | 0.5–1.0 | 3 | - | - | 65.0 | [50] |
Leucaena leucocephala leaves | 10 | 10–500 | 6 | PSO | Chemisorption | 25.5 | [51] |
Ceratophyllum demursum | 0.1 | 1.46–1.73 | 7 | - | Physical, monolayer | 44.8 | [52] |
Punica geranatum leaves | 0.1 | 1.46–1.73 | 7 | - | Physical, monolayer | 31.78 | [53] |
Mirabilis jalapa leaves | 0.1 | 50–125 | 4.5 | - | Multimolecular layers | 38.5 | [54] |
Laurus nobilis leaves waste | 4 | 5–10 | 6 | PFO | Chemisorption | 96.1 | [55] |
Cladophora fascicularis | 2 | 225.4–838.3 | 5 | PSO | Chemisorption | 227.7 | [56] |
Nickel ferrite nanoparticle | - | 0.01–0.05 | 5 | PSO | - | 19.2 | [57] |
MoS2/thiol-functionalized multiwalled carbon nanotube | 2 | 20–150 | 6 | PSO | Multilayer | 90.0 | [58] |
Nano Zero-Valent Iron particles | 0.1 | 10–1000 | 6 | PSO | Chemisorption | 140.8 | [59] |
Cu-Based Composite Track-Etched Membranes | 0.0033–0.0052 | 50 | 5 | PSO | Chemisorption | 0.44–0.56 | [60] |
Raw-AC | 10 | 25–1000 | 6 | PSO | Chemisorption | 96.4 | In this study |
AC-OH | 10 | 25–1000 | 6 | PSO | Chemisorption | 170.9 | In this study |
Material | Initial Concentration of Pb (mg/L) | Mass (g) | Temperature (K) | pH of the Solution | Contact Time (min) | qe (mg/g) |
---|---|---|---|---|---|---|
1 | 24.414 | 0.1 | 298.15 | 2 | 1440 | 2.41213 |
1 | 48.204 | 0.1 | 298.15 | 2 | 1440 | 4.33674 |
1 | 92.492 | 0.1 | 298.15 | 2 | 1440 | 7.1832 |
1 | 412.92 | 0.1 | 298.15 | 6 | 1440 | 34.3227 |
1 | 531.76 | 0.1 | 298.15 | 6 | 1440 | 41.574 |
1 | 854.16 | 0.1 | 298.15 | 6 | 1440 | 62.287 |
2 | 26.958 | 0.05 | 298.15 | 2 | 1440 | 5.36626 |
2 | 44.613 | 0.05 | 298.15 | 2 | 1440 | 8.84096 |
2 | 89.1 | 0.05 | 298.15 | 2 | 1440 | 17.61704 |
2 | 333.02 | 0.05 | 298.15 | 6 | 1440 | 63.7672 |
2 | 757.88 | 0.05 | 298.15 | 6 | 1440 | 127.848 |
2 | 1012.98 | 0.05 | 298.15 | 6 | 1440 | 147.475 |
Material | Initial Concentration of Pb | Mass | Temperature | pH of the Solution | Contact Time | qe | |
---|---|---|---|---|---|---|---|
Material | 1.00 | −0.01 | −0.24 | 0.01 | −0.01 | 0.01 | 0.27 |
Initial concentration of Pb | −0.01 | 1.00 | 0.14 | −0.02 | −0.12 | 0.27 | 0.79 |
Mass | −0.24 | 0.14 | 1.00 | −0.06 | 0.06 | 0.38 | −0.18 |
Temperature | 0.01 | −0.02 | −0.06 | 1.00 | 0.16 | 0.22 | −0.03 |
pH of the solution | −0.01 | −0.12 | 0.06 | 0.16 | 1.00 | −0.23 | −0.03 |
Contact time | 0.01 | 0.27 | 0.38 | 0.22 | −0.23 | 1.00 | 0.22 |
qe | 0.27 | 0.79 | −0.18 | −0.03 | −0.03 | 0.22 | 1.00 |
Biomass | Initial Concentration of Pb (mg/L) | Mass (g) | Temperature (K) | pH of the Solution | Contact Time (min) | Optimal qe (mg/g) |
---|---|---|---|---|---|---|
Raw-AC | 854.16 | 0.1 | 298.15 | 6 | 1440 | 62.287 |
AC-OH | 1012.98 | 0.05 | 298.15 | 6 | 1440 | 147.475 |
Experiments | Experimental Parameters | ||||
---|---|---|---|---|---|
pH | Time (min) | Ci (mg/L) | Dose (g/L) | Temperature (K) | |
Solution pH | 2–6 | 1440 | 25–1000 | 10 | 300.15 |
Dose | 6 | 1440 | 25–1000 | 5–20 | 300.15 |
Contact times | 6 | 5–1440 | 25–250 | 10 | 300.15 |
Temperature | 6 | 1440 | 25–1000 | 10 | 300.15–330.15 |
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Vázquez-Sánchez, A.Y.; Lima, E.C.; Abatal, M.; Tariq, R.; Santiago, A.A.; Alfonso, I.; Aguilar, C.; Vazquez-Olmos, A.R. Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm. Molecules 2023, 28, 6387. https://doi.org/10.3390/molecules28176387
Vázquez-Sánchez AY, Lima EC, Abatal M, Tariq R, Santiago AA, Alfonso I, Aguilar C, Vazquez-Olmos AR. Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm. Molecules. 2023; 28(17):6387. https://doi.org/10.3390/molecules28176387
Chicago/Turabian StyleVázquez-Sánchez, Alma Y., Eder C. Lima, Mohamed Abatal, Rasikh Tariq, Arlette A. Santiago, Ismeli Alfonso, Claudia Aguilar, and América R. Vazquez-Olmos. 2023. "Biosorption of Pb(II) Using Natural and Treated Ardisia compressa K. Leaves: Simulation Framework Extended through the Application of Artificial Neural Network and Genetic Algorithm" Molecules 28, no. 17: 6387. https://doi.org/10.3390/molecules28176387