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In the ongoing discourse surrounding environmental health, heightened attention has been directed toward contamination of aquatic ecosystems with antibiotics, particularly tetracycline. Graphene oxide (GO) has emerged as a promising avenue for absorbing these antibiotics from water. This work explores the potential of ZnO and Al2O3 nanoparticles for tetracycline removal. Initially, Al(OH)3 and Zn(OH)2 were synthesized through hydrothermal processes and dried at 90 °C, followed by annealing at 400 °C to yield Al2O3 and ZnO nanoparticles, respectively. The prepared samples were characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR). Results indicated that nanoparticles in their hydroxide forms, heated to 90 °C, displayed superior tetracycline removal efficiency compared to oxide forms heated to 400 °C. This preference may stem from heightened activity or the ionic potential (charge/radius) of aluminum hydroxide nanoparticles, irrespective of heating temperature (400 or 90 °C). The findings could pave the way for novel approaches to removing antibiotic contaminants from water, offering solutions to mitigate pharmaceutical pollution's adverse effects on aquatic ecosystems and public health.

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  • Nanoscale aluminum and zinc oxides by hydrothermal processes have been synthesized.

  • The formation of nanoparticles is shown to be accompanied by differential tetracycline adsorption.

  • Adsorption favors oxide surfaces, suggesting more complex mechanisms for antibiotic residue removal.

  • The findings underscore surface chemistry's importance in nanoparticle-based water filtration systems.

aluminum oxide, environment pollutant, nanoparticle, tetracycline, zinc oxide
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Water, essential to all life forms, covers approximately 71% of the Earth's surface, yet only a mere 1% is suitable for human consumption (Hossain 2015; Juzsakova et al. 2023). Clean water, devoid of hazardous compounds, chemicals, and pathogens, is indispensable for sustaining life (Balaram et al. 2023; Silva 2023). However, providing safe drinking water remains a paramount challenge for societies today, exacerbated by increased anthropogenic activities leading to environmental contamination (Devlin & Brodie 2023). Among the pollutants threatening water quality, antibiotics pose a significant concern due to their widespread use and persistence (Briffa et al. 2020). Despite their relatively low concentrations in water, antibiotics such as tetracycline exhibit high stability and toxicity, impacting aquatic biota and human health (Kim et al. 2011). Chemotherapeutic drugs that inhibit or stop the growth of microorganisms are known as antibiotics (Chen & Huang 2011). Antibiotics come in a variety of forms and can be categorized according to their chemical makeup, mode of action, spectrum of activity, and mode of administration (Ahmad et al. 2021). Their mode of action is the most often used of these classifications, and the most prevalent classes are based on the following: monobactams, carbapenems, lincomycin, macrolides, polypeptides, polyenes, rifamycin, tetracyclines, chloramphenicol, quinolones (Cheng et al. 2020) In today's society, pharmaceuticals, including antibiotics like tetracycline, play a crucial role in healthcare and wellness, further contributing to their presence in water bodies (Swapna Priya & Radha 2014). Tetracyclines are broad-spectrum antibiotics extensively employed in both human and veterinary medicine (Gao et al. 2012). Several techniques exist for the elimination of tetracycline from water sources, among these approaches are ion exchange, bioremediation, photocatalytic degradation, graphene oxide, adsorption (Kumari et al. 2023). Traditional water and wastewater treatment methods often fail to completely remove prescription antibiotics (Juzsakova et al. 2023). To address this challenge, researchers have explored various materials, including chitosan particles, rectorite, palygorskite, smectite clay, and montmorillonite, for adsorption and elimination of tetracycline antibiotics (Briffa et al. 2020). However, there remains a pressing need for cost-effective and efficient treatment methods to combat this contamination (Daghrir & Drogui 2013). ZnO, as a common semiconductor material, has gained increasing research attention because of its outstanding electrical and optical properties, low cost, high biological safety, environment benign and strong photocatalytic degradation ability of organic pollutants under UV light (Zafar et al. 2019). Aluminum oxide has a high surface area and porosity, allowing it to effectively adsorb antibiotic molecules from aqueous solutions (Chen & Huang 2010). Nanoparticles (NPs) such as aluminum oxide (Al2O3) and zinc oxide (ZnO) have emerged as highly promising adsorbents for the removal of water contaminants due to their exceptional ability to dissociate and capture chemical and biological pollutants present in aqueous environments (Liu et al. 2012).This recognition stems from their unique surface properties, large surface area-to-volume ratio, and tunable surface chemistry, which facilitate strong interactions with a wide range of contaminants (Katz & Miquel 1994; Al-Abduljabbar & Farooq 2022). Alumina (Al₂O₃) and zinc oxide (ZnO) are widely studied materials in the field of adsorption and catalysis due to their unique structural, chemical, and surface properties. Their high surface area, stability, and catalytic potential make them suitable candidates for removing contaminants, including pharmaceutical residues, from aqueous environments. Recent studies have shown the efficacy of alumina in adsorbing a range of pharmaceuticals, including antibiotics like tetracycline, due to its high surface area and acidic properties (Chen & Huang 2010; Ahmadi et al. 2022). This study explores the adsorption of tetracycline onto alumina, examining factors like surface interaction and adsorption performance. Alumina's amphoteric nature allows it to interact with various drug molecules through different bonding mechanisms, enhancing its adsorption capacity. Studies have demonstrated that alumina-based adsorbents can achieve high removal efficiencies, with some showing high adsorption under optimal conditions (Chen & Huang 2010). This paper highlights modifications to alumina that improve its adsorption of ciprofloxacin and other pharmaceuticals.

Similarly, zinc oxide (ZnO) is recognized for its photocatalytic properties, especially in degrading pharmaceutical contaminants through advanced oxidation processes (Ameta & Ameta 2018) discusses the photocatalytic degradation of various pharmaceutical contaminants using ZnO under UV light. ZnO has been used to degrade antibiotics, non-steroidal anti-inflammatory drugs (NSAIDs), and other organic pollutants. Under UV or visible light irradiation, ZnO generates reactive oxygen species (ROS), which effectively degrade pharmaceutical compounds into less harmful byproducts (Tanveer et al. 2019). This study focuses on the degradation efficiency of ZnO for ibuprofen under UV light, emphasizing ROS generation and drug degradation pathway. In summary, both alumina and zinc oxide have shown promise in adsorbing and degrading pharmaceuticals, with studies highlighting their effectiveness, reaction mechanisms, and potential limitations. (AlMohamadi et al. 2024) This research compares doping modifications in alumina and ZnO, revealing significant performance improvements in the adsorption and photocatalysis of antibiotics. In summary, both alumina and zinc oxide have shown promise in adsorbing and degrading pharmaceuticals, with studies highlighting their effectiveness, reaction mechanisms, and potential limitations (Zhang et al. 2016).

Among the diverse array of water treatment methodologies available, adsorption has gained prominence as an effective and versatile approach for addressing the challenges posed by antibiotic wastewater contamination (Balali-Mood et al. 2021). Its appeal lies in its simplicity of operation, high removal efficiency, and cost-effectiveness, making it particularly suitable for large-scale applications in both industrial and municipal settings (Liang et al. 2016). However, the successful implementation of adsorption-based water treatment strategies critically hinges on the careful selection of appropriate adsorbents. Ideal adsorbents must fulfill several criteria, including abundance, affordability, ease of synthesis, and environmental compatibility, to ensure their practical viability and sustainability in real-world applications (Huang et al. 2018; LaPlante et al. 2022). Therefore, the quest for novel adsorbent materials with superior performance characteristics remains a focal point in the ongoing research aimed at advancing water treatment technologies. In this work, we used hydrothermal techniques to synthesize Al2O3 and ZnO NPs. Optimal conditions for tetracycline removal were identified through meticulous measurements of adsorption efficiency from liquid phases. We aim to contribute to the development of economical and efficient treatment methods for removing antibiotic contaminants from water, safeguarding both environmental health and human well-being (Alanazi et al. 2024).

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Materials

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Aluminum chloride hexahydrate (AlCl3·6H2O), 98.0%, and urea (NH2CONH2), 98.7% were obtained from Sigma-Aldric. Cetyltrimethyl ammonium bromide, 99.0%, and ethanol (C2H5OH) (97%) were obtained from (BDH). Also, zinc acetate Zn(CH3COO)2, 98.0%, was purchased from Sigma–Aldrich, Taufkirchen. Tetracycline was purchased from Samarra Pharmaceutical Company; sodium hydroxide (NaOH), 99.0%, and potassium permanganate (KMnO4), 99.7% were obtained from Sigma–Aldrich.

Creation of NPs using aluminum oxide (Al2O3 NPs)

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An autoclave thermal hydrolysis process was used to create aluminum oxide (Al2O3) NPs (Dani et al. 2020; Ndlwana et al. 2021). Aluminum chloride hexahydrate, cetyltrimethylammonium bromide, and urea could be used for this. To begin, dissolve 3 g (12.44 mmol) of AlCl3·6H2O in 50 ml of distilled water, 1.25 g (20.833 mmol) of NH2CONH2, and 1 g (2.74 mmol) of CTAB in 10 ml of distilled water separately. After that, combine these solutions and whisk for 15 min using a magnetic stirrer. The homogenous mixed solution was placed inside an autoclave lined with Teflon. After heating the autoclave for 6 h at 200 °C in the furnace, the white precipitate was cleaned three times using deionized water. Following that, the precipitate was dried for 60 min at 90 °C., after which it was heated to 400 °C for 120 min, the equations demonstrate:
(1)
(2)
(3)

Preparation about zinc oxide NPs

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Zinc oxide (ZnO) NPs artificial were prepared by a hydrothermal methodology (autoclave) (Xu et al. 2004), by the reaction of Zn(CH3COO)2 after dissolving 3 g (16.36 mmol) in 50 ml of pure water, additionally melting 0.981 g (16.35 mmol) of NH2CONH2 in purified water (10 ml) and 1 g (2.74 mmol) of CTAB in 20 ml of distilled water. This solution was stirred using a magnetic stirrer at 25 °C (10 min) in a beaker until it became a homogenous mixed solution. Thereafter, the mixed solutions were added into a Teflon-lined autoclave, sealed and put into an oven at 200 °C for 6 h. Then, allow the autoclave to cool naturally to room temperature. The white precipitate was washed six times with distilled water. After that, the precipitate was dried at 90 and 400 °C for 60 and 120 min, respectively, according to the following equations:
(4)
(5)
(6)
(7)

Absorption of tetracycline contaminants

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The synthesized NPs were used to adsorb tetracycline from aqueous solutions. The percentage tetracycline removal (%R) measurements were made using a UV-vis spectrometer. The spectrometer strategy at λmax = 610 nm wavelength (Ahmad et al. 2021) used the following process: Take two 50-ml vessels marked as test (T) and blank (B). Add 0.01 g of NPs to the B and T containers. Using a volumetric flask, fill every T and B vessel holding 25 ml of distilled water. Add 1 ml of tetracycline to the T container, and 1 ml of distilled water to the B container. On a shaker set to 250 rpm, each of these containers was shaken for 15 min. To separate the NPs, the samples underwent centrifugation following the vibration period. Add 2 ml of mixture solution (NaOH and KMnO4) to all containers, and an ultraviolet visible spectrophotometer operating at 610 nm wavelength was used to measure the absorbance of each of these containers after they were shaken for 5 min at 250 rpm. The percentage tetracycline removal (%R) can be deliberated as follows Equation (8) (Abdullah et al. 2021):
(8)
where C0 is the premier tetracycline concentration (ppm), Ct is the final tetracycline concentration (ppm).
The adsorbed amount of tetracycline at balance was calculated using neutralization (9) (Abdullah et al. 2021):
(9)
where v is the volume of the tetracycline solution (ml), w is the weight of NPs (g), qt is the adsorption amplitude of tetracycline (Li et al. 2023).

Kinetic adsorption and equilibrium isotherms

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The interactions between the sorbates and adsorbents are described by various mathematical models, such as adsorption kinetics and equilibrium isotherms. The kinetic models are relatively efficient when determining the rate at which the adsorbent efficiently removes the adsorbate, such as tetracycline. Following three different kinetic methods were applied to study the adsorption kinetics of pollutants on the sorbents: pseudo-first-order reaction, Equation (10), pseudo-second-order reaction, Equation (11) and intra-particle diffusion reaction Equation (12):
(10)
(11)
(12)
where qe and qt denote the amount of tetracycline adsorbed (mg/g) at equilibrium and at time t (min), respectively. While K1, k2, and Kd are the equilibrium rate constants of pseudo-first-order, pseudo-second-order, and the intra-particle diffusion rate constants in min−1, g/mg min, mg/g min1/2, respectively.
The Langmuir and Freundlich isotherm models were used to study the equilibrium isotherm, and the Langmuir model was also used to describe the monolayer's homogeneous adsorption processes, showing that the adsorbent layer is stably located on the surface, with sites that are identical and no lateral interactions between the molecules. The Langmuir model is expressed in Equation (13). While the Freundlich isotherm model is used to describe the non-ideal and reversible adsorption processes and is not limited to monolayer adsorption. Therefore, the Freundlich isotherm model is used to describe the multilayer adsorption, with a non-uniform distribution of the adsorption heat and sites of different affinities on heterogeneous adsorbent surfaces. The Freundlich isotherm model is expressed by Equation (14). Flory–Huggins isotherm model, which occasionally deriving the degree of surface coverage characteristics of adsorbate onto adsorbent, can express the feasibility and spontaneous nature of an adsorption process. That used for the calculation of spontaneity free Gibbs energy, is related to the equation, Equation (15):
(13)
(14)
(15)
where Ce, qe, and qmax, b are the residual of tetracycline concentrations in solution (mg/l), the amount of tetracycline adsorbed (mg/g) on the sorbent at equilibrium, the maximum amount of the tetracycline per unit weight of sorbent (mg/g) and b is the Langmuir adsorption equilibrium constant (L/mg), respectively. While Kf and n are the Freundlich adsorption constants (mg/g) (mg/l)1/n and adsorption intensity, respectively. R, T, and K represent the gas constant (8.314 J/mol.K), absolute temperature, and K equilibrium constant for Freundlich or Langmuir, respectively.
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Characteristics optics of the nanoparticle

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The optimal prosperity (transmittance) of NP solutions (approximately 1 × 10−5 M) in ethanol for as and annealed samples between wavelengths 250 and 750 nm were measured. The energy gap can be computed using the following formula (Mahmoud 2017):
(16)
where λmax is the maximum transmittance in nm, and 1,240 is the conversion coefficient utilized to transform nm to eV. As can be seen in Figure 1 that the energy gap of Al(OH)3, determined to be 4.4 eV due to neutralization, exhibits a distinct contrast with that of Al2O3, which stands at 4.1 eV, as depicted in Figure 1(a). The observed increase in transmittance with wavelength (redshift) from 278 to 299 nm suggests alterations in the electronic structure of the material. Notably, the enhancement in optical transmittance with increasing annealing temperatures underscores the pivotal role of structural homogeneity and particle crystallization in modifying the optical properties of Al(OH)3 NPs. These findings are in line with previous studies, affirming the credibility of the observed trends (Kim et al. 2018). Similarly, the optical spectrum of ZnO NPs portrays a notable redshift in the permeability edge, ranging from 219 to 372 nm, with escalating annealing temperatures, as depicted in Figure 1(b). The energy gap of the as-prepared Zn(OH)2 is determined to be 5.66 eV, contrasting with the reduced energy gap of the annealed sample (ZnO), measured at 3.33 eV. These observations corroborate with established references (Rini et al. 2021), further substantiating the experimental results and providing valuable insights into the impact of annealing on the optical properties of ZnO NPs. Overall, the systematic investigation of the optical behavior of Al(OH)3 and ZnO NPs reveals intricate relationships between material composition, structural characteristics, and thermal treatment, underscoring the importance of these factors in tailoring the optical properties of nanomaterials for wastewater treatment applications.
Figure 1
Transmittances for the following nanoparticles: (a) aluminum oxide and (b) zinc oxide, blue color at 90 °C, red color at 400 °C.
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Transmittances for the following nanoparticles: (a) aluminum oxide and (b) zinc oxide, blue color at 90 °C, red color at 400 °C.

Figure 1
Transmittances for the following nanoparticles: (a) aluminum oxide and (b) zinc oxide, blue color at 90 °C, red color at 400 °C.
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Transmittances for the following nanoparticles: (a) aluminum oxide and (b) zinc oxide, blue color at 90 °C, red color at 400 °C.

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The FTIR spectra

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The grade and kind of alumina NPs made by the hydrothermal process were identified using FTIR spectroscopy (Figure 2). Figure 2(a) depicts the Al(OH)3 FTIR vision. The O–H bond enlargement and bending vibrations in the water molecules in the sample can be assigned a wide range at around 3,289 and 1,665 cm–1 (Boumaza et al. 2009; Haile et al. 2015). A band corresponding to the typical γ-alumina wavelength of 1,180 cm−1 is formed by the Al–O vibration mode. The Al–O–Al bond in alumina's gamma phase produces the band at 619 cm–1. The peak located at 746 cm–1 is ascribed to bending vibrations of Al–O bonds. The FTIR vision of specimens annealed at 400 °C, Figure 2(b), showed a decrease in broad band at 3,485 and at 1,639 cm−1 which corresponding to O–H stretching vibration water were observed (Toledo et al. 2018). The band from 840 to 520 cm−1 corresponds to functional groups of (O–Al–O) bands. Figure 2(c), shows the FTIR spectrum of Zn(OH)2 NPs. The peak at 3,310 and 1,502 cm−1 refers to O–H bond vibrations stretching (Dhawale et al. 2018). While Zn–O has a peak at 833 cm−1 (Abdullah et al. 2021). The peak at 3,310 cm−1 and 1,502 at 468 cm−1 accounts for Zn–O expansion (Jan et al. 2020), and a peak at 455 cm−1, which identify to ZnO NPs (Singh 2011). The expansion of C = O, C–O for acetate corresponds to the weakened shakings within 1,502− and 1,390 cm−1 (Handore et al. 2014). FTIR spectrum for ZnO, is presented in Figure 2(d). The stretching and bending of the O–H bond in water are responsible for the wide bands at 3,448− and 1,639 cm−1, respectively. On the other hand, the peak at 570 cm−1, corresponds to the stretching ZnO mode (Jayarambabu et al. 2014).
Figure 2
FTIR analysis of Al2O3 (a) as prepared and (b) heated at 400 °C. FTIR analysis of ZnO (c) as prepared and (d) heated at 400 °C.
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FTIR analysis of Al2O3 (a) as prepared and (b) heated at 400 °C. FTIR analysis of ZnO (c) as prepared and (d) heated at 400 °C.

Figure 2
FTIR analysis of Al2O3 (a) as prepared and (b) heated at 400 °C. FTIR analysis of ZnO (c) as prepared and (d) heated at 400 °C.
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FTIR analysis of Al2O3 (a) as prepared and (b) heated at 400 °C. FTIR analysis of ZnO (c) as prepared and (d) heated at 400 °C.

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X-ray diffraction analysis

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The X-ray diffraction patterns for Al(OH)3 and Al2O3 NPs

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The X-ray diffraction (XRD) of Al(OH)3 was used for mensuration to belay the sample's crystal structure and phase assembly. Figure 3(a) shows the diffraction pattern that was obtained together with the indexed peaks. Three separate summits (120), (031), and (002) were detected at 2θ = 28.26°, 38.41°, and 49.10°, respectively, concerning γ-AlOOH, according to JCPDS # 21-1307 (Umar et al. 2015). The XRD modality of the Al2O3 revealed that the specimen was in cube crystalline symmetry, with the central grid of the face Figure 3(b). For the XRD pattern 7.901 Å was the calculated network parameter. The JCPDS # 00-056-0457, and the observed values agree (311), (222), (400), (511), and (440) planes match the Al2O3 observed peaks at (2θ = 37.03°, 39.34°, 45.90°, 61.16°, 66.98°). With two main diffraction intensity peaks at 2θ = 45.90° and 66.98°, the NPs were primarily gamma phase, respectively (Fang et al. 2016).
Figure 3
XRD for (a) Al(OH)3, (b) Al2O3, (c) Zn(OH)2, and (d) ZnO nanoparticles.
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XRD for (a) Al(OH)3, (b) Al2O3, (c) Zn(OH)2, and (d) ZnO nanoparticles.

Figure 3
XRD for (a) Al(OH)3, (b) Al2O3, (c) Zn(OH)2, and (d) ZnO nanoparticles.
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XRD for (a) Al(OH)3, (b) Al2O3, (c) Zn(OH)2, and (d) ZnO nanoparticles.

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The Debye–Scherrer formula is widely recognized for its ability to determine crystallite size by utilizing the full width at half maximum (FWHM) of diffraction peaks (Mansour et al. 2017):
(17)
where D is the crystallite's size, K is the form factor (0,98), is the X-ray wavelength, and β is the line boarding at a single peak's half-maximum intensity (FWHM) at 2θ (where θ is the Bragg corner) (Baghdadi et al. 2022; Rahem et al. 2022) The lattice constants a, b, and c of γ-AlOOH and a of Al2O3 NPs were computed as indicated in Table 1 and Equations (18) and (19), respectively, and the average grain size of the NPs was determined by using the Scherrer equation (17):
(18)
(19)
where hkl represents Miller indices, d represents spacing parameter distance, and a, b, c are lattice constants.
Table 1

The results of the XRD for aluminum oxide and zinc oxide heated at 90 and 400 °C

Heated at2θ (°)hklFWHM (°)d (Å)D (Å)Lattice constant
a (Å)b (Å)c (Å)
Aluminum oxide 90 °C for 60 min 49.106 200 0.80500 1.85375 108.43 3.707 12.016 2.90 
38.413 031 0.58890 2.34148 142.77 – – – 
28.263 120 0.66760 3.15495 122.64 – – – 
400°C for 120 min 66.89 440 1.54320 1.39750 59.55 7.901 7.901 7.901 
45.90 400 1.44790 1.97537 54.83 – – – 
37.03 311 4.45720 2.42535 18.78 – – – 
Zinc oxide 90°C for 60 min 36.44 101 0.63810 2.46337 130.99 3.3046 – 5.191 
33.05 100 0.66670 2.70768 124.22 – – – 
63.17 103 0.40710 1.47056 228.95 – – – 
400°C for 120 min 36.33 101 0.61880 2.47060 135.04 3.2428 – 5.206 
31.83 100 0.54370 2.80873 151.85  – – 
34.48 002 0.55420 2.59874 150.00  – – 
Heated at2θ (°)hklFWHM (°)d (Å)D (Å)Lattice constant
a (Å)b (Å)c (Å)
Aluminum oxide 90 °C for 60 min 49.106 200 0.80500 1.85375 108.43 3.707 12.016 2.90 
38.413 031 0.58890 2.34148 142.77 – – – 
28.263 120 0.66760 3.15495 122.64 – – – 
400°C for 120 min 66.89 440 1.54320 1.39750 59.55 7.901 7.901 7.901 
45.90 400 1.44790 1.97537 54.83 – – – 
37.03 311 4.45720 2.42535 18.78 – – – 
Zinc oxide 90°C for 60 min 36.44 101 0.63810 2.46337 130.99 3.3046 – 5.191 
33.05 100 0.66670 2.70768 124.22 – – – 
63.17 103 0.40710 1.47056 228.95 – – – 
400°C for 120 min 36.33 101 0.61880 2.47060 135.04 3.2428 – 5.206 
31.83 100 0.54370 2.80873 151.85  – – 
34.48 002 0.55420 2.59874 150.00  – – 
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The XRD patterns for Zn(OH)2 and ZnO NPs

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The XRD pattern shows special peaks of Zn(OH)2 at (2θ = 33.227°, 34.925°, 36.444°, 47.612°, 56.692°, and 62.323°) that identify the diffraction planes (100), (002), (101), (102), (110), and (103), respectively (Figure 3(c)). These peaks are compatible with those reported in JCPDS Card No. 79-2205 and are indexed as the hexagonal wurtzite phase of Zn(OH)2 NPs with lattice constants a = b = 3.3046 Å and c = 5.0266 Å (Miller & Rocheleau 1997). Moreover, the X-ray of Zn(OH)2 is quite comparable to ZnO (JCPDS Card No. 01-089-0510) (Bindu & Thomas 2014; Kashinath et al. 2015).

According to Figure 3(b) and Table 1, the average grain size of ZnO NPs was obtained using the Scherrer equation (17). The lattice constants (a and c) of ZnO NPs were obtained using Equation (20), as shown in Table 1:
(20)

Atomic force microscope surface morphology

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Aluminum oxide (Al2O3) NPs

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Al2O3 generated and annealed at different temperatures are depicted in atomic force microscope (AFM) images in Figure 4(a) and 4(b). Bubbles in Figure 4(a) represent the dispersion and accumulation of aluminum oxide NPs, whereas Figure 4(b) shows homogeneous morphologies. Possible explanations for this include the fact that most hydroxides Al(OH)3 undergo conversion to oxides Al2O3 when heated to 400 °C. The produced grain size is 38.656 nm, but after annealing, it grows to 53.82 nm, as shown in Table 2. The sample may need less heat to turn all hydroxides into oxide, which could explain this.
Table 2

Variation of grain size for Al2O3 and ZnO heated at 90 and 400 °C

SampleAverage grain size (nm)
Ionic potential (charge/radius)
As prepared (90 °C)Annealing (400 °C)
Al2O3 38.65 53.82 0.044 
ZnO 28.29 35.44 0.022 
SampleAverage grain size (nm)
Ionic potential (charge/radius)
As prepared (90 °C)Annealing (400 °C)
Al2O3 38.65 53.82 0.044 
ZnO 28.29 35.44 0.022 
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Figure 4
Atomic force microscopy pictures in three dimension and granularity collection division charts for Al2O3: (a) at 90 °C, (b) at 400 °C, and ZnO (c) at 90 °C, (d) at 400 °C.
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Atomic force microscopy pictures in three dimension and granularity collection division charts for Al2O3: (a) at 90 °C, (b) at 400 °C, and ZnO (c) at 90 °C, (d) at 400 °C.

Figure 4
Atomic force microscopy pictures in three dimension and granularity collection division charts for Al2O3: (a) at 90 °C, (b) at 400 °C, and ZnO (c) at 90 °C, (d) at 400 °C.
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Atomic force microscopy pictures in three dimension and granularity collection division charts for Al2O3: (a) at 90 °C, (b) at 400 °C, and ZnO (c) at 90 °C, (d) at 400 °C.

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Zinc oxide (ZnO) NPs

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Figure 4(c) and 4(d) show AFM pictures of ZnO produced and annealed at varied temperatures. Zinc oxide nanoparticle stampede and cumulation modification from an oval shape (Figure 5(a)) to the biggest (Figure 4(d)).
Figure 5
SEM image for (a) Al2O3 and (b) ZnO annealing at 400 °C for 120 min.
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SEM image for (a) Al2O3 and (b) ZnO annealing at 400 °C for 120 min.

Figure 5
SEM image for (a) Al2O3 and (b) ZnO annealing at 400 °C for 120 min.
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SEM image for (a) Al2O3 and (b) ZnO annealing at 400 °C for 120 min.

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This may have to do with the fact that most hydroxides Zn(OH)2 are converted to oxides ZnO when heated to 400 °C, as Table 2 illustrates. That is, during the preparation technique (autoclave), the product can be transformed into zinc oxide NPs, and the grain size grows from 28.29 to 35.44 nm as the temperature rises, as shown in Table 2.

Surface scanning electron microscopy surface morphology

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The metal oxide NPs (ZnO and Al2O3) produced by the hydrothermal process and heated at 400 °C for 2 h are shown in the scanning electron microscopy (SEM) surface morphological image at a magnification of 500 nm. They are shown in Figure 5. These results demonstrate that raising the annealing temperature caused a high porosity structure to form on the sample's surfaces. Several clumps and clusters of NPs were visible on the surface. The SEM image of the annealed Al2O3 NPs in Figure 5(a) displays cluster formation, whereas Figure 5(b) displays the morphology of the ZnO particles in the sample, which exhibits a very wide particle size distribution and various platelet and flaky particle morphologies.

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The tetracycline contaminant was absorbed by NPs in both cases (as prepared and annealed), Figure 6(a) and Table 3, according to the study by Silva (2023). Briefly, potassium permanganate reacts with tetracycline in alkaline solution to produce potassium manganite (green color) with a new band at 610 nm. 0.01 g and 28 ml of the product contains KMnO4 solution (3.30 mmol), NaOH (196 mmol), and TC (0.112 mmol), for each prepared nanoparticle were utilized. The results indicated that Al(OH)3 is the superior nanoparticle to transfer the highest efficiency of tetracycline pollution, as shown in Figure 6(c) and Table 3.
Figure 6
Optimum condition results: (a) best nanoparticles adsorbed to tetracycline (TC), (b) best weight of Al(OH)3, (c) best TC concentration, (d) best shake time, (e) best shake speed, (f) best KMnO4 concentration.
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Optimum condition results: (a) best nanoparticles adsorbed to tetracycline (TC), (b) best weight of Al(OH)3, (c) best TC concentration, (d) best shake time, (e) best shake speed, (f) best KMnO4 concentration.

Figure 6
Optimum condition results: (a) best nanoparticles adsorbed to tetracycline (TC), (b) best weight of Al(OH)3, (c) best TC concentration, (d) best shake time, (e) best shake speed, (f) best KMnO4 concentration.
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Optimum condition results: (a) best nanoparticles adsorbed to tetracycline (TC), (b) best weight of Al(OH)3, (c) best TC concentration, (d) best shake time, (e) best shake speed, (f) best KMnO4 concentration.

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A preliminary investigation was conducted to ascertain the optimal circumstances for tetracycline adsorption. Several factors were changed and only one factor remained constant each time when estimating the optimum conditions for removing tetracycline. These factors are: Al(OH)3 weight, tetracycline concentration, change in shaking time, and change in shaking speed.

Effect of weight

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The impact of varying sample weights of Al(OH)3 on tetracycline removal was investigated in this study. Initially, five containers were labeled as B (blank) and T (test), and different weights of Al(OH)3 (0.0100, 0.0150, 0.0200, 0.0250, and 0.0300 g) were added to both T and B containers. Subsequently, a 1 ml aliquot of tetracycline solution from a mother solution of 560 ppm (0.0011 M) was added to the T containers, followed by the addition of 25 ml of distilled water to all containers. The solutions were then shaken at 250 rpm for 15 min and subsequently separated by centrifugation. Next, 2 ml of a mixture solution (NaOH and KMnO4) from the mother solution (NaOH 0.07M, and KMnO4 0.0336M) was added to all containers, and the mixture was agitated at room temperature, resulting in a final volume of 28 ml. The maximum absorbance was observed at λmax = 610 nm. The absorbance value exhibited a gradual increase with increasing weight of Al(OH)3 up to 0.02 g, followed by a slight decline. Consequently, this weight was determined as the optimal weight, as depicted in Figure 6(b).

Effect of tetracycline concentration

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All procedures outlined in the previous section (section A) were replicated, with the exception that the weight of Al(OH)3 remained at 0.02 g, while varying tetracycline concentrations were tested (4, 8, 12, 16, 20, 24, and 28 ppm). The percentage of removal (%R) showed an increase with rising TC concentration up to 20 ppm, followed by a decrease as TC concentration further increased. Thus, a TC concentration of 20 ppm was determined to be the most effective, as illustrated in Figure 6(c) and Table 3.

Table 3

The percentage removal (%R) and adsorption capacity (qt) of tetracycline pollution on nanoparticles (heated at 90 and 400 °C)

Metal oxide typePercentage removal (%R)
Adsorption capacity (qt)
As preparedAnnealingAs preparedAnnealing
Al2O3 56.95 47.21 1,628.42 129.88 
ZnO 51.50 45.57 1,020.70 1,130.98 
Metal oxide typePercentage removal (%R)
Adsorption capacity (qt)
As preparedAnnealingAs preparedAnnealing
Al2O3 56.95 47.21 1,628.42 129.88 
ZnO 51.50 45.57 1,020.70 1,130.98 
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Effect of the shake time

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The procedures outlined in the previous section were replicated, maintaining the weight of Al(OH)3 at 0.02 g, and using a fixed tetracycline concentration of 20 ppm. The shaking durations were varied (5, 10, 15, 20, 25, and 30 min) at a speed of 250 rpm. The percentage of removal (%R) increased as the shaking time increased, reaching a maximum at 15 min, after which it declined with further increases in shaking time. Hence, a shaking time of 15 min was deemed optimal, as depicted in Figure 6(d).

Effect of shake speed

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The experiments conducted in the previous section were replicated, with the weight of Al(OH)3 set at 0.02 g, the TC at 20 ppm, and a shaking time of 15 min. Different shaking speeds were applied (50, 100, 150, 200, 250 rpm) at 250 rpm. The percentage of removal (%R) increased with the shake speeds, reaching its peak at 250 rpm. Thus, 250 rpm was determined to be the optimal shake speed, as illustrated in Figure 6(e).

Effect of potassium permanganate concentration

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The experiments conducted in the previous section were replicated, with the weight of Al(OH)3 set at 0.02 g, the TC at 20 ppm, the shake time at 15 min, and the shake speeds at 250 rpm. Different concentrations of KMnO4 were tested (1.25, 2.50, 3.75, 5.0, 6.25, 7.50, and 8.75 ppm). The percentage of removal (%R) increased with the concentration until it peaked at 6.25 ppm. Therefore, this concentration was determined to be the most effective, as depicted in Figure 6(f).

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Since the Al(OH)3 sample has shown good adsorption properties toward TC removal, kinetic studies were performed for the data collected using this sample. Figure 7 shows the change in the TC amount adsorbed by the sample as a function of time and indicates that the adsorption of adsorbate was faster in the first 5 min. A further adsorbed quantity increases more slowly as the surface becomes saturated with the adsorbed molecules. It can be seen from Figure 7 that the time required to reach the adsorption equilibrium between the Al(OH)3 and TC was around 30 min.
Figure 7
Adsorption capacity of TC against time for Al(OH)3.
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Adsorption capacity of TC against time for Al(OH)3.

Figure 7
Adsorption capacity of TC against time for Al(OH)3.
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Adsorption capacity of TC against time for Al(OH)3.

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In this study, pseudo-first-order, pseudo-second-order and Weber–Morris intra-particle diffusion methods were applied. The experimental data were fitted to Equations (13)–(15). The corresponding graphical representations are displayed in Figure 8. The parameter values obtained from these equations models are showed in Table 4.
Table 4

Comparison of the kinetic model equations on the sorption of TC from solution onto Al(OH)3

Pseudo-first order
Pseudo-second order
Intra-particle diffusion
K1 (min−1)qe cal (mg/g)R2K2 (g/mg min)qe cal (mg/g)R2Kd (mg/g min1/2)IR2
0.049 1.982 0.9917 1.010 3.247 0.9988 0.0302 0.891 0.9893 
Pseudo-first order
Pseudo-second order
Intra-particle diffusion
K1 (min−1)qe cal (mg/g)R2K2 (g/mg min)qe cal (mg/g)R2Kd (mg/g min1/2)IR2
0.049 1.982 0.9917 1.010 3.247 0.9988 0.0302 0.891 0.9893 
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Figure 8
(a) Pseudo-first-order plot, (b) pseudo-second-order plot, (c) intra-particle diffusion plot for TC adsorption onto Al(OH)3.
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(a) Pseudo-first-order plot, (b) pseudo-second-order plot, (c) intra-particle diffusion plot for TC adsorption onto Al(OH)3.

Figure 8
(a) Pseudo-first-order plot, (b) pseudo-second-order plot, (c) intra-particle diffusion plot for TC adsorption onto Al(OH)3.
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(a) Pseudo-first-order plot, (b) pseudo-second-order plot, (c) intra-particle diffusion plot for TC adsorption onto Al(OH)3.

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The pseudo-first-order model in the TC adsorption are equal to 0.9917, compare with the values of the pseudo-second-order result is 0.9988. That is mean the pseudo-second-order model exhibited the best fit with the experimental data on TC adsorption compare with the pseudo-first-order models. The third kinetic model was used to identify the diffusion mechanism. The value of I is an indication of the occurrence of the boundary layer effect during absorption of TC onto Al(OH)3. The larger I value means the greater the contribution surface absorption in the rate-controlling step.

Langmuir and Freundlich isotherms for TC was checked, and shown in Figure 9. The isotherm data were fitted better by the Freundlich equation, with a correlation coefficient (R2) of 0.9956, indicating that the adsorption of TC was a heterogeneous process, and that the monolayer adsorption predicted by Langmuir model was not a reliable option. Freundlich n indicates the intensity of adsorption and has a value of 1.337. If the value for n > 1, the adsorption is a favorable physical process, Figure 9 and Table 5.
Table 5

Correlation coefficients and constant parameters calculated for various adsorption models

Langmuir
Freundlich
qmax (min−1)KL (L/mg)R2 (kJ/mol)KF (mg/g)NR2 (kJ/mol)
4.058 0.472 0.9709 1.858 1.316 1.337 0.9956 −3.312 
Langmuir
Freundlich
qmax (min−1)KL (L/mg)R2 (kJ/mol)KF (mg/g)NR2 (kJ/mol)
4.058 0.472 0.9709 1.858 1.316 1.337 0.9956 −3.312 
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Figure 9
(a) Langmuir isotherm models for TC adsorption on Al(OH)3 and (b) Freundlich isotherm models for TC adsorption onto Al(OH)3.
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(a) Langmuir isotherm models for TC adsorption on Al(OH)3 and (b) Freundlich isotherm models for TC adsorption onto Al(OH)3.

Figure 9
(a) Langmuir isotherm models for TC adsorption on Al(OH)3 and (b) Freundlich isotherm models for TC adsorption onto Al(OH)3.
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(a) Langmuir isotherm models for TC adsorption on Al(OH)3 and (b) Freundlich isotherm models for TC adsorption onto Al(OH)3.

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In this work, the synthesis of NPs via hydrothermal autoclaving presents a versatile route for tailored nanomaterial production. Al(OH)3 and Zn(OH)2 NPs were synthesized through meticulous heating protocols, yielding Al2O3 and ZnO NPs following annealing at 400 °C. Advanced characterization techniques including AFM, SEM, FTIR, UV-visible spectroscopy, and XRD unveiled distinct morphologies, with Al(OH)3 NPs exhibiting an average grain size of 38.65 nm and Zn(OH)2 NPs displaying a platelet-like morphology with an average grain size of 28.29 nm. Notably, hydroxide NPs demonstrated superior tetracycline removal efficiency compared to their oxide counterparts, attributed to enhanced ion binding facilitated by hydrogen bond formation on hydroxide surfaces. These findings highlight the pivotal role of precursor selection and synthesis conditions in tailoring nanoparticle properties, with implications for applications ranging from water treatment to environmental remediation.

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The authors declare that they have not used any AI tools for the scientific writing of this paper.

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All relevant data are included in the paper or its Supplementary Information.

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The authors declare there is no conflict.

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