Optimized colorimetric detection of cobalt ions (Co²⁺) using alliin–Ag–Au nanoparticles

Abstract

Cobalt is a vital trace element that is necessary for biological functions but poses toxicity risks at higher levels, necessitating its accurate detection in environmental and occupational contexts. Traditional cobalt detection methods, including spectroscopy and chromatography, often lack affordability and environmental friendliness. This study explores the development of a highly sensitive, selective, and eco-friendly nanosensor for detecting cobalt ions (Co²⁺) using bimetallic silver–gold nanoparticles (Ag–Au NPs) synthesized with alliin extracted from garlic. The properties of the synthesized Ag–Au NPs were investigated using a comprehensive suite of techniques including UV-visible spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, electron microscopy and computational quantum mechanical modelling (DFT). The nanoparticles exhibited a distinct surface plasmon resonance peak at 533 nm, confirming their formation. Sensitivity studies revealed a detection limit of 0.09 nM for Co²⁺ ions, outperforming many established methods. Selectivity tests demonstrated a significant absorbance shift and colour change in the presence of Co²⁺ ions, differentiating it from other metals like Mn²⁺, Cr³⁺, Al³⁺, Ni²⁺, Pd²⁺, Cd²⁺, Ba²⁺, and Zn²⁺. The reaction kinetics indicated pseudo-first-order behaviour, emphasizing the specificity of the interaction between Co²⁺ and Ag–Au NPs. Molecular orbital analysis and Gibbs free energy calculations affirmed the stability of the Co-containing complexes. This novel colorimetric sensor, leveraging garlic-derived biomaterials, provides a sustainable, efficient and environment friendly means for detecting cobalt ions, with potential use in environmental assessment and healthcare applications.

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1. Introduction

Cobalt is a transition metal with diverse industrial and medical applications. It is an essential trace element for humans and other animals. Cobalt is primarily obtained as a by-product of nickel and copper mining. Cobalt ions, specifically in the form of Co²⁺ and Co³⁺ ions, are commonly encountered in various occupational settings and environmental sources. Cobalt ions, in particular, are essential components of vitamin B₁₂ (cobalamin). In addition, it serves as a cofactor for various enzymes, including those involved in DNA synthesis and energy production, playing a crucial role in numerous biological processes. However, excessive exposure to cobalt ions can lead to toxicity, resulting in adverse health effects.^1,2 Excess heavy metal accumulation, particularly cobalt (Co) accumulation in agricultural soils and water bodies, driven by both natural and human factors, presents a major challenge. Although essential for vitamin B₁₂, high cobalt exposure significantly reduces crop biomass and yield, leading to substantial financial losses for farmers.³ This essential metal is becoming an emerging contaminant in the coastal environment due to its rising levels in the environment.^4–7 Toxicity evaluation shows that cobalt has varying acute and chronic effects on different marine organisms, impacting diatoms, copepods and mysid shrimp, while sensitivity to chronic cobalt exposure varies greatly across species, with algae being most sensitive in marine environments and small plants in freshwater.⁸

Occupational exposure to cobalt may occur through various industrial processes, including the manufacturing of cutting tools, hard metals and batteries, etc. Workers involved in mining, smelting and alloy production are at a higher risk of cobalt ion exposure. Anthropogenic sources, including cobalt mining, industrial emissions and disposal of electronic waste, contribute to environmental contamination. Cobalt ions can also enter the environment through natural processes, such as weathering of cobalt-containing minerals.⁹ Excessive cobalt ion levels can disrupt cellular processes by interfering with enzymes and proteins dependent on metal cofactors. Exposure to cobalt ions can disrupt cellular redox balance, causing an overproduction of reactive oxygen species (ROS) and promoting lipid peroxidation. High cobalt exposure has been associated with cardiotoxicity, including the development of cardiomyopathy and heart failure. Chronic cobalt exposure can trigger polycythemia, a condition where the body overproduces red blood cells. Limited studies suggest that cobalt ions may have neurotoxic effects, potentially leading to cognitive impairment and neuropathy.^10,11 The cobalt toxicity is observed by haem oxidation and blockage of inorganic calcium channels, cytotoxicity and genotoxicity.¹² Regulatory bodies have set the maximum allowable concentration for cobalt in freshwater at 110 µg L⁻¹, with drinking water limits established at 1000 µg L⁻¹ in New Zealand and 100 µg L⁻¹ in the US.¹³

Recent research has made significant progress in developing plasmonic nanoparticle (NP) and fluorescent probe-based detection systems for heavy metal ions in environmental samples. These nanosensors exploit the unique optical, electronic, and surface plasmon resonance (SPR) properties of nanomaterials—especially gold, silver, and quantum-dot-based systems—to achieve ultrahigh sensitivity. For example, Ma et al. reported a near-infrared (NIR) fluorescent probe for the sensitive and interference-free detection of heavy metals, offering deep-tissue imaging capability and high photostability.¹⁴ Similarly, Qi et al. developed a fluorescent, highly conductive anisotropic Janus-type nanoribbon hydrogel array film (JNHAF) that integrates optical and electrochemical responsiveness, providing a robust platform for multi-analyte environmental monitoring.¹⁵ These advances demonstrate that plasmonic and fluorescent nanoprobes can transduce chemical interactions—such as ion coordination, redox reactions, or fluorescence quenching—into quantifiable optical or electronic signals. The interaction mechanisms often rely on energy or charge transfer between the metal ion and the nanomaterial, leading to measurable shifts in absorption, fluorescence emission, or refractive index. Such mechanisms offer rapid response times, portability, and visual detection, making them ideal for field-based sensing of toxic metal ions.^14,15

Parallel advancements have also been made in nanosensor probes for detecting pollutants and contaminants in biological matrices and for health care practices, where biocompatibility, sensitivity, and selectivity are paramount. For instance, Gao et al. developed luminescent nanoparticles for selective lighting of tumour cells, demonstrating the potential of fluorescent nanoprobes in biomedical diagnostics.¹⁶ Similarly, Wang et al. fabricated a fluorescence sensor based on semi-covalent dummy molecularly imprinted silica on silane-modified carbon quantum dots, achieving high selectivity for target analytes through molecular imprinting on fluorescent nanostructures.¹⁷ Gao et al. further advanced the field with a dual-signal (fluorescence and refractive index) detection system based on a porous silicon Bragg mirror, enabling simultaneous optical and photonic signal readouts for analytes like β-lactoglobulin.¹⁸ These systems illustrate how hybrid nanocomposites, integrating silica matrices, quantum dots, and photonic structures, have revolutionized biological sensing. They allow for dual or multi-modal detection—combining fluorescence, reflectance, and refractive index signals—to ensure higher accuracy and reduced false positives. The underlying sensing mechanisms generally involve changes in the local dielectric environment, fluorescence resonance energy transfer (FRET), or photo-induced electron transfer (PET) upon analyte binding, translating nanoscale interactions into measurable macroscopic optical responses.^16–18

At present, the commonly used methods in the field of cobalt ion detection include electrochemical methods,^19–21 fluorescence spectroscopy,^22–25 chromatography^26–29 and spectrometry.^30–32 Co²⁺ ion nanosensors can play a crucial role in various scientific, industrial and environmental applications due to their ability to detect and quantify the presence of Co²⁺ ions at very low concentrations.³³ Co²⁺ ion nanosensors are specialized nanoscale devices designed to detect and quantify the presence of Co²⁺ ions in various environments. These nanosensors utilize a range of nanomaterials, such as nanoparticles, nanowires or nanotubes, functionalized with specific molecules or ligands that exhibit a strong affinity for Co²⁺ ions. The binding of Co²⁺ ions to these nanomaterials leads to measurable changes in electrical, optical or other properties, allowing for the sensitive and selective detection of Co²⁺ ions.^34–41 Recent work on the development of Co²⁺ ions includes a colorimetric sensing platform based on glutathione (GSH) modified silver nanoparticles (AgNPs),³⁶ dopamine dithiocarbamate (DDTC)–AgNPs as a colorimetric sensor with a detection limit of 14 mM,³⁷ AgNPs functionalized with lignin (L–AgNPs) for the determination of Co²⁺ in aqueous solution using colorimetry and SERS,³⁸ AuNPs functionalized using glycyrrhizic acid (GA) with a detection limit of 0.4 nM,³⁹ and an AgNPs optic nanosensor with a detection limit of around 0.68 µM based on its interaction with glutathione, cysteine and Co²⁺ ions.⁴⁰ Likewise, Xu et al. designed a Ag–Au bimetallic nanosensor, confirmed by dynamic light scattering, that detected Co²⁺ ions with a limit of 0.02 mM.⁴¹ The sensing mechanism relied on the reaction of Co²⁺ with ethylenediamine (en) and S₂O₃²⁻ to produce (en)₂CoS₂O₃²⁺ on the nanoparticle surface. This formation reduced the nanoparticle's negative charge, decreasing repulsion and causing them to aggregate.

The present study focused on developing a nanosensor for Co²⁺ ions, prioritizing features like precise selectivity, accuracy, a lower detection limit and green synthesis. To achieve this, Ag–Au bimetallic nanoparticles and garlic-derived biomaterials were utilized, and a surface plasmon resonance (SPR) based colorimetric method was selected due to its cost-effectiveness and practicality.

2. Materials and methods

2.1 Materials

The Naharlagun market, located in Arunachal Pradesh, India, was the source of the garlic. AgNO₃ (99.99%), Al₂(SO₄)₃, CrCl₂ (99.9%), MnCl₂·4H₂O (99%), CoCl₂·H₂O (97%), ZnSO₄·7H₂O (99.10%), BaCl₂·2H₂O (99%), CdCl₂, Pb(NO₃)₂ and NiCl₂·6H₂O (98%) of analytical standard were purchased from Sigma-Aldrich.

2.2 Preparation of garlic extract and synthesis of silver–gold bimetallic nanoparticles (Ag–Au NPs)

Forty grams of garlic cloves were macerated in a mortar and pestle to a fine consistency. This paste (pH 5.8) was then thoroughly mixed with 400 mL of deionized water. The resulting suspension was subjected to continuous stirring at 60 °C for 45 minutes with the aid of a magnetic stirrer, and subsequently allowed to equilibrate to room temperature (24–25 °C). Finally, the solution was passed through Whatman filter paper (20–25 µm pore size) to collect the desired filtrate (garlic extract, pH 6.8).⁴² The silver–gold bimetallic nanoparticles (Ag–Au NPs) were synthesized by reducing the metal ions of AgNO₃ and AuCl₃ using the garlic extract. For this, 10 mL of an aqueous solution containing 1 mM AgNO₃ and 1 mM AuCl₃ (mixed in a 1 : 1 volume ratio) was prepared. To this mixture, 4 mL of freshly prepared garlic extract was added under constant stirring at room temperature. The resultant solution had a pH of 6.5. The change in colour of the solution from pale yellow to wine-red indicated the formation of Ag–Au bimetallic nanoparticles.

2.3 Characterization of the synthesized Ag–Au NPs

A comprehensive analysis of the synthesized nanoparticles was conducted using the techniques: UV-visible spectroscopy, Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction study (XRD), scanning electron microscopy (SEM), energy dispersive X-ray (EDX) and transmission electron microscopy (TEM). UV-visible spectra were acquired between 300 and 800 nm using a UV-2600 spectrometer from Shimadzu Corporation (Japan) to monitor the formation of the nanoparticles, assess their optical characteristics, and determine their surface plasmon resonance. FTIR and Raman spectroscopy were used to determine the possible functional groups present in the nanoparticles. FTIR spectra were obtained in the 4000–400 cm⁻¹ range to detect functional groups on the nanoparticle surface using a Spectrum Two, PerkinElmer Inc., USA. The Raman spectra were acquired using a DXR2 Smart Raman Spectrometer (Thermo Scientific Inc., USA) with an excitation wavelength of 532 nm. A Bruker D8 Advance powder X-ray diffractometer with a Cu Kα X-ray source (λ = 1.5406 Å) and a scanning speed of 3° min⁻¹ was used to analyse the nanoparticles’ crystalline structure. High-resolution TEM pictures were acquired using a JEM-2100 (JEOL Ltd, Japan) with a 200 kV accelerating voltage. The nanoparticle sizes, interplanar distance, and d spacing in a selected area electron diffraction (SAED) investigation were determined from TEM images using ImageJ software. The elemental composition of the nanoparticles was determined using Aztec (Oxford Instruments, UK). The nanoparticles’ morphology was examined using a Sigma 300 VP field effect scanning electron microscope (FE-SEM) (ZEISS, Germany) with a resolution of 1.2 nm at 15 kV. The elemental composition, empirical formula, chemical and electronic states of the elements in the nanoparticles were determined using an ESCALAB Xi+ XPS (Thermo Fisher Scientific Inc., USA). The theoretical structure of the nanoparticle and its interaction with Co²⁺ ions were simulated using density functional theory (DFT).

2.4 Sensitivity of Ag–Au NPs for the detection of Co²⁺ ions

The response of the synthesized Ag–Au NPs to various metal ions, namely Al³⁺, Cr²⁺, Mn²⁺, Co²⁺, Zn²⁺, Ba²⁺, Cd²⁺, Pd²⁺, and Ni²⁺ was evaluated by observing changes in the surface plasmon resonance (SPR) band. This study optimized the detection of Co²⁺ using Ag–Au NPs by analyzing the impact of temperature and pH on the absorbance peak. The spectra were studied at temperatures of 20, 40, 60, 80 and 100 °C and pH 2, 4, 7 and 9 for this purpose.

To estimate the limit of detection (LoD) for sensing Co²⁺ ions, 30–90 µl of 0.01 mM Co²⁺ solution (6–18 nM) was added separately to a 3 mL solution of the synthesized Ag–Au NPs. Spectrophotometric analysis showed an absorbance maximum at 533 nm. To ascertain the minimum detectable concentration of Co²⁺, a linear calibration curve was plotted and the LoD was subsequently computed from the curve's slope and standard deviation (SD) of the control, following the established equation (eqn (1)):⁴³

LoD = 3.3 × SD/S (1)

Here, SD represents the standard deviation of the control group's absorbance maxima (n = 8), while S is the slope of the calibration curve.

2.5 Interference study

Interference by common anions and cations was investigated using a methodology that involved preparing a 3 mL aqueous suspension of diluted Ag–Au NPs. This suspension was initially spiked with 0.2 mL of 1 mM CoCl₂. Subsequently, 0.2 mL of 1 mM salt solutions (Al₂(SO₄)₃, ZnSO₄, Pb(NO₃)₂, NiCl₂, MnCl₂, CdCl₂ and BaCl₂) were individually introduced into separate samples. The nanoparticles and metal ions were thoroughly mixed by vortexing each sample for 2 minutes at ambient temperature. Absorbance spectra were then acquired in the range of 300–800 nm utilizing a UV-vis spectrophotometer. A total of six replicates were analyzed for both the control group (Co²⁺ only) and the experimental groups containing the additional salts. The absorbance data were statistically evaluated using analysis of variance (ANOVA) within Origin 9 software to assess for significant variations after the introduction of the various ionic species.

2.6 Analysis of real water samples

To validate the sensor performance, real water samples were collected from seepage groundwater in Rono Hills, the Dikrong river and packaged mineral drinking water from the Doimukh area, Arunachal Pradesh. In this study, varying volumes (200 µL, 300 µL, 400 µL, and 500 µL) of 0.01 mM Co²⁺ solution were spiked into 500 µL of Au–AgNPs solution prepared using the real water samples. Spectroscopic analysis was performed by recording absorbance spectra from 300 to 800 nm and the data were subsequently analyzed to determine the relationship between peak absorbance and the concentration of Co²⁺ ions.

2.7 Kinetics of the reactive interaction between the Ag–Au NPs and Co²⁺

Experiments to study the kinetics of the interaction between the synthesized Ag–Au NPs and Co²⁺ involved mixing 25, 50, and 75 µL of 1 mM Co²⁺ solution with 3 mL of diluted Ag–Au NPs, followed by absorbance measurements at five-minute intervals. The data on peak absorbance at different time intervals were analyzed using zero-, first-, and second-order kinetic models, whose linear representations are shown in eqn (2)–(4):⁴⁴

[A_t] = [A₀] − k₀t (2)

ln([A_t]/[A₀]) = − k₁t (3)

1/[A_t] = k₂t + 1/[A₀] (4)

The following terms define kinetic analysis: [A_t]: absorbance at time t; [A₀]: initial absorbance (t = 0); k₀: zero-order rate constant; t: time. Reaction kinetics were analyzed by plotting: [A_t] vs. T for zero-order models, ln([A_t]/[A₀]) vs. T for first-order models, and 1/[A_t] vs. T for second-order models. The rate constants were derived from the slopes of these linear regression plots.

2.8 Computational details

The Ag–Au NPs were synthesized by reducing the metal ions of AgNO₃ and AuCl₃ using garlic extract. Alliin was reported as one of the active biocompounds of the garlic extract for the synthesis of metal nanoparticles.⁴² Therefore, density functional theory (DFT) methods were used for study of the bonding interaction of the Au and Ag particles with alliin. All the calculations were performed using the MO6-2X functional in combination with the def2-TZVP basis set.⁴⁵ The Gaussian 16 suite of programs was used for this purpose.⁴⁶ To understand the molecular orbital analysis, natural bond orbital (NBO) analysis was performed.⁴⁷

The Gibbs free energy of the proposed reaction of Ag⁺ and Au³⁺ with alliin systems was calculated using the following equations (eqn (5) and (6)):

ΔG₁ = [{G_{Ag–Au–NPs}} –{G_alliin + G_Ag,Au}] (5)

ΔG₂ = [{G_{Ag–Au–NPs–Co}} – {G_{Ag–Au–NPs} + G_Co}] (6)

where, G_{Ag–Au–NPs}, G_Alliin and G_Ag,Au are the Gibbs free energies of Ag–Au NPs, alliin and Au, Ag particles, respectively, for eqn (2). Similarly, G_{Ag–Au–NPs–Co}, G_{Ag–Au–NPs} and G_Co are the Gibbs free energy of the Ag–Au–NPs–Co nanoparticles, Ag–Au NPs and Co, respectively. The structures were optimized to their energetically stable conformations, with all real eigenvalues of the Hessian matrix. The geometries of alliin, Ag–Au NPs and Ag–Au–NPs–Co nanoparticles were optimized with the help of the MO6-2X functional in combination with the def2-TZVP basis set.⁴⁵

3. Results and discussion

3.1 Formation of Alliin–Ag–Au NPs

Alliinase is an enzyme found in plants of the Allium genus, such as garlic (Allium sativum) and onions (Allium cepa). It facilitates the enzymatic conversion of precursors into the distinctive flavours, aromas and biologically active compounds associated with these plants, particularly when they are crushed or damaged. It catalyzes the conversion of alliin (a sulfur-containing amino acid derivative) into allicin, a compound responsible for the pungent smell of fresh garlic. Alliinase activity can be inhibited by heat, which is why cooking garlic often has less pungency. In this study, boiling the garlic paste promptly after crushing served to inactivate the enzyme and halt the conversion of alliin to allicin.^42,48,49

The addition of garlic extract transformed the clear AgNO₃ + AuCl₃ solution into a wine-red solution, indicating the reduction of metal ions by alliin to form Ag–Au NPs. In contrast, the colourless AgNO₃ solution turned to a brownish colour after addition of alliin containing garlic extract.⁴² However, similar colour has been reported for Ag–Au alloy nanoparticles synthesized using the leaf extract of Cannabis sativa.⁵⁰ The successful formation of Au–Ag bimetallic nanoparticles (Au–AgNPs) was indicated by the characteristic bell-shaped UV-vis absorption spectrum exhibiting a surface plasmon resonance (SPR) band at λ_max = 533 nm (Fig. 1). In comparison, pure AgNPs typically display a strong SPR band in the 400–500 nm region,⁵¹ attributed to localized surface plasmon resonance. Conversely, pure AuNPs typically exhibit SPR peaks within the 520–530 nm range, with a gradual red shift observed as particle size increases.⁵² When Au and Ag are combined to form a single bimetallic nanoparticle, their plasmonic behaviour changes. The localized surface plasmon resonance (LSPR) is no longer governed by a single metal but by the collective oscillations of electrons from both Au and Ag atoms.⁵³ The interaction and coupling of the plasmons from the two different metals alter the electronic environment and the overall refractive index of the nanoparticles. This shifts the resonant frequency, and thus the peak wavelength, to a new position. The final SPR peak can be located anywhere between the peaks of the two pure metals, or even outside this range, depending on the metals’ ratio, their arrangement (alloy or core–shell), and the particle's size and shape. For Ag–Au nanoalloys, a single, broader SPR peak is typically observed that shifts with the changing ratio of Au and Ag. In this case, the shift from the pure metal peaks to a single peak at 533 nm is the expected signature of an Ag–Au alloy formation. The 533 nm peak is indicative of the synthesis of a new material with unique optical properties. UV-vis spectroscopy is a valuable technique for elucidating the structure of bimetallic Au–Ag nanoparticles (NPs). Core–shell NPs are identified by two separate surface plasmon resonance (SPR) absorption peaks. In contrast, alloy type NPs display a single SPR peak that falls between the individual SPR peaks of pure gold and silver. The observation of a single SPR peak at 533 nm in our bimetallic Au–Ag NPs confirms the formation of an alloy structure. A key factor in the formation of these alloy type bimetallic NPs is the near-identical lattice constants of Au and Ag, which enable their homogeneous mixing within the particle volume.⁵⁴ The Ag NPs, Au NPs and Ag–Au NPs synthesized using the leaf extract of Cannabis sativa were reported to exhibit SPR peaks at 426 nm, 552 nm and 538 nm respectively.⁵⁰ Khan et al. found that Ag–Au bimetallic nanoparticles, when synthesized with a high concentration of Pulicaria undulata extract, exhibited a single, broad surface plasmon absorption band centered around 490 nm.⁵⁵

Fig. 1 UV-vis spectrum of the Ag–Au NPs synthesized using alliin containing garlic extract.

3.2 Raman spectrum of the synthesized nanoparticles

The Raman spectrum (Fig. 2A) exhibits several characteristic bands corresponding to different vibrational modes. The S=O peak in a Raman spectrum typically appears in the 700–1100 cm⁻¹ range. A strong band appearing at ∼850 cm⁻¹ may correspond to S=O. The region from 1000–1500 cm⁻¹ shows weak to moderate intensity bands, arising mainly from C–C and C–H bending or deformation modes. A weak peak around 1730 cm⁻¹ indicates C=O stretch, while the O–H stretching vibration is seen as a broad peak around 2500 cm⁻¹. The NH₂ peak position in the Raman spectra depends on the specific molecule, but typical peaks for the symmetric and antisymmetric N–H stretching vibrations are around 3310 and 3360, respectively.⁴²

Fig. 2 (A) Raman spectrum and (B) FTIR spectrum of the Ag–Au NPs synthesized using garlic extract.

The FTIR spectrum (Fig. 2B) of the synthesized nanoparticles showed the presence of the absorbance peaks at 3287 (O–H stretching), 2153.64 (carbonyl group, C=O of the carboxylic group), 1635.89 (amide II) and 1008 (S=O stretching) cm⁻¹.⁵⁶ This is indicative of the presence of an alliin-like compound that also possess these functional groups.

3.3 Scanning and transmission electron microscopy (SEM and TEM)

The nanoparticles in the scanning electron micrograph resembled fragmented flakes or sheets with pointed angles and varying orientations across the surface (Fig. 3A and B). However, Alias and Rashed reported Ag–Au bimetallic nanoparticles of spherical shape.⁵⁷ Pham et al. reported triangular Ag–Au NPs that vary from a Au core shell structure to alloy nature based on the Ag–Au ratio.⁵⁸ According to their study, gold deposition on silver nanoplates was observed to be influenced by the amount of HAuCl₄ added. L-Ascorbic acid reduced Au³⁺ ions to gold atoms, which selectively deposited on the high-energy facets at the edges of the triangular silver nanoplates. This preferential deposition occurs due to the low coordination number at the tips of the silver nanoplates, aiding in the preservation of their triangular shape. Energy-dispersive X-ray spectrometry (EDX) was used to determine the chemical composition of the nanoparticle surface. As shown in Fig. 3(C) the nanoparticle surface contained 54.79% C K, 19.32% N K, 25.73% O K, 0.05% S K, 0.09% Ag L, 0.01% Au M by weight.

Fig. 3 (A) and (B) Scanning electron micrograph of the Ag–Au NPs synthesized using garlic extract, (C) energy-dispersive X-ray spectrometry (EDX) spectrum of the synthesized nanoparticles that showed the presence of Ag, Au, C, O, N and S, (D) transmission electron micrograph (brightfield), inset: histogram showing nanoparticle size calculated using ImageJ software, (E) TEM indicating interplanar distance (311), (F) selected area diffraction (SAED) pattern of individual Ag–AuNPs.

Fig. 3D and E show TEM images of the Ag–Au NPs. The size distribution of the Ag–Au NPs varied from 10.46 to 34.33 nm, with a mean particle size of 21.31 ± 1.09 nm (Fig. 3D inset). The interplanar distance from the TEM image was calculated to be 0.117 ± 0.003 nm (311 plane), which is similar (0.11 ± 0.021 nm) to the value calculated from the XRD (0.12 nm). The selected area diffraction (SAED) pattern of individual Ag–Au NPs showed the fcc crystal lattice structure of the Ag–Au NP sample with 111, 200, 220 and 311 planes (Fig. 3F).

3.4 X-ray photon spectroscopy and X-ray diffraction study

XPS analysis of the sample confirmed the presence of several elements, including Au, Ag, O, S, N and C, as shown in the survey scans (Fig. 4A–G). The Au 4f core level exhibited characteristic spin–orbit split peaks at 84.5 eV (4f_7/2) and 88.1 eV (4f_5/2), consistent with metallic gold (Au⁰). The Ag 3d spectrum showed twin peaks at 368.4 eV (3d_5/2) and 374.4 eV (3d_3/2), indicative of metallic silver (Ag⁰) and suggesting it as the major silver component, possibly in the form of Ag NPs. Additional peaks were observed at 531.75 eV (O 1s), 168.45 eV (S 2p), 399.95 eV (N 1s) and 284.8 eV (C 1s), respectively.^59–61 The X-ray diffraction (XRD) pattern presented in Fig. 4H provides evidence for the formation of Ag–Au NPs. Well-defined diffraction peaks were recorded at 2θ values of 38.24°, 44.43°, 64.99°, and 77.66°. These peaks are attributed to the (111), (200), (220) and (311) planes, respectively, consistent with the face-centered cubic (FCC) lattice arrangement of both Ag and Au. These findings align with previous reports on Ag–Au bimetallic alloy nanoparticles.⁶² The interplanar distances based on the 2θ values were calculated to be 0.24 nm, 0.20 nm, 0.14 nm, and 0.12 nm for the (111), (200), (220) and (311) planes.

Fig. 4 (A) XPS survey scan of Ag–Au NPs, (B) XPS Au 4f scan, (C) XPS (S 2p scan), (D) XPS C 1s scan, (E) XPS Ag 3d scan, (F) XPS N 1s scan, (G) XPS O 1s scan, and (H) X-ray diffraction crystallography of the Ag–Au NPs.

3.5 Analytical performance in selectivity for metal ions

The sensitivity of Alliin–Ag–Au NPs was evaluated for the metal ions: Co²⁺, Mn²⁺, Cr³⁺, Al³⁺, Ni²⁺, Pd²⁺, Cd²⁺, Ba²⁺, and Zn²⁺. Only the Co²⁺ caused a change in the colour of the solution from wine red to blue. This was accompanied by a broad absorbance peak, a red shift and a decrease of the intensity of the surface plasmon resonance peak of the Aliin–Ag–Au NP solution from 530 to 558 nm (Fig. 5). Glutathione (GSH)-functionalized Au/Ag nanoframes with open nanostructures were developed by Lee et al. via galvanic replacement. These structures exhibited the ability to selectively detect cobalt (Co²⁺) ions.⁶³ They reported that Co²⁺ ions changed the absorbance band in the visible and infrared range (500–800 nm) and the colour of the GSH–Au/AgNF solution from gray to brown. The detection of Co²⁺ ions by Sung et al. relied on a colorimetric response of GSH–AgNP, specifically a solution colour transition from pale blue to dark green.³⁶ They also reported that the UV absorbance band was largely changed, even though the main peak at 750 nm in the UV-vis spectra was maintained.

Fig. 5 (A) and (B) Selectivity of the Ag–Au NPs to Co²⁺ ions based on (A) UV-visible spectra of the Ag–Au NPs in the presence of different metal ions and (B) the colour of the solutions containing Ag–Au NPs after addition of different metal ion solutions, (C) and (D) estimation of LoD, (C) UV-visible absorption spectra of Ag–Au NPs upon the addition of an Co²⁺ solution at a concentration of 6–18 nM, (D) absorbance versus concentration of Co²⁺.

Observations from Fig. 5C indicate that the absorption maximum of the alliin–Ag–Au NPs diminishes as the concentration of Co²⁺ ions rises. Over the 6–18 nM range, a highly significant negative linear correlation exists between the absorbance at 540–550 nm and Co²⁺ concentration (r = −0.99). This means that higher Co²⁺ concentrations result in lower absorbance, accompanied by a redshift in the peak wavelength. The data can be fitted to a linear regression model: Y = 0.995 – 0.0198X, with a high coefficient of determination (adjusted R² = 0.98), where Y signifies peak absorbance (nm) and X represents Co²⁺ concentration (nM) (Fig. 5D). This method achieves a low limit of detection (LoD) for Co²⁺ ions of 0.09 nM (SD = 0.000548, slope = 0.0198), outperforming several existing techniques (Table 1).

Table 1 Comparison of the limit of detection (LoD) for cobalt (Co²⁺) analysis using alliin–Ag–Au NPs and previously reported methods

S. no.	Nanoparticles	LoD	Calibration range	Absorbance
Method: surface-enhanced Raman scattering (SERS)
1	Lignin capped Ag NPs^38	0.16 µM	1 to 30 µM	A 420nm–452nm
Method: fluorescence
2	Glutathione stabilized Au nanoclusters (GSH–Au NCs)^35	0.124 µM	2 to 50 µM	Maximum emission wavelength at 500 nm and maximum excitation at 412 nm
Method: UV-visible spectrometry
3	Cellulose based nanosensor decorated with 1-(2-hydroxy-1-naphthylazo)-2-naphthol-4-sulfonic acid (HNNSA)^34	1.13 µM	0.0–0.1 µM	A 402nm
4	Dopamine dithiocarbamate (DDTC) Ag NPs^37	14 µM	1.0 mM to 15 mM	A 570nm/A390nm
5	Au NPs modified with glycyrrhizic acid^39	0.4 nM	50 mM to 16 µM	A 550nm/A525nm
6	Ag NPs–glutathione interaction and later on secondary interaction of this structure with cysteine and Co^2+ ion^40	0.68 µM	1.7 µM to 20 µM	A 299nm
7	Alliin–Ag–Au NPs (present study)	0.09 nM	06–18 nM	A 533nm

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3.6 Reaction dynamics

The absorbance spectra in the presence of Co²⁺ ions showed specific patterns at different pH and temperature (Fig. 6A and B). The absorbance peaks were narrower at pH 7 and 45 °C. With a change in temperature, the LSPR peak of the Ag–Au nanoparticles exhibited a noticeable blue shift toward shorter wavelengths. This spectral behaviour suggests a decrease in the local refractive index surrounding the nanoparticles, likely due to temperature-induced desorption of adsorbed molecules from the nanoparticle surface. The reduction in surface adsorption at elevated temperatures weakens the interaction between the nanoparticles and the analyte molecules, leading to diminished refractive index sensitivity.^64,65 Additionally, the slight reduction in absorbance intensity observed at higher temperatures can be attributed to thermally induced changes in particle morphology or partial aggregation, which disrupts plasmonic coupling.⁶⁶ Consequently, the sensing potential of the Ag–Au nanoparticles decreases with increasing temperature, as elevated thermal energy reduces both the stability of analyte binding and the overall plasmonic response of the system. The non-monotonic shift of the plasmon resonance suggests that multiple competing factors influence the optical behaviour of the nanoparticles.

Fig. 6 (A) and (B) UV-visible spectra of the Ag–Au NPs in the presence of Co²⁺ ions at different (A) pH and (B) temperature, (C) changes in the peak absorbance of Ag–Au NPs in the presence of 25 µM, 50 µM and 75 µM Co²⁺ at 5 minute intervals, and the first-order reaction kinetics model, and (D) UV-visible spectra of the Ag–Au NPs + Co²⁺ in the presence of other ions.

The interaction between alliin–Ag–Au NPs and Co²⁺ was studied kinetically, revealing that the reaction adheres to pseudo-first-order kinetics. Based on the superior R² value, the first-order model exhibited the best goodness-of-fit compared to its zero-order and second-order counterparts (Fig. 6C), even though two reactants (nanoparticles and Co²⁺ ions) are involved. This indicates a rate dependence predominantly on the concentration of one specific reactant.⁴⁴

The selectivity of the alliin–Ag–Au NP sensor for Co²⁺ ions was confirmed by an analysis of variance (ANOVA), which revealed no statistically significant difference in absorbance between the alliin–Ag–Au NP + Co²⁺ solution and the solution containing individually added other ions (F = 2.139; P = 0.06; df = 7, 40) (Fig. 6D). This result supports the conclusion that the binding affinity of the sensor is higher towards Co²⁺ ions, overcoming anionic interference. This demonstrates a remarkable degree of discrimination critical for the rigorous and meticulous determination of Co²⁺ within diverse aquatic environments.

3.7 Analysis of real water samples

The UV-visible absorption spectra (Fig. 7A, C and E) demonstrate a concentration-dependent response of the synthesized Ag–AuNP sensor to Co²⁺ ions across three different real water samples: seepage ground water (SGW), Dikrong river water (DRW), and mineral drinking water (MDW). In all three cases, the addition of increasing volumes (200–500 µL) of 0.01 mM Co²⁺ solution led to a noticeable decrease in the absorbance intensity of the characteristic surface plasmon resonance (SPR) peak, accompanied by peak broadening. Notably, the original Au–AgNP solutions prepared in the real water samples (black lines in Fig. 7A, C and E) exhibited sharp and intense absorbance peaks without significant broadening. This suggests the absence of Co²⁺ ions or their presence at concentrations below the sensor's limit of detection (LoD) in the unspiked water samples. The higher peak absorbance and well-defined SPR bands further confirm that the colloidal stability of the nanoparticles remained intact in these natural matrices. The linear regression plots (Fig. 7B, D and F) show a strong negative correlation between the absorbance and the volume of Co²⁺ added, with R² and Pearson's correlation coefficient (r) values of 0.99 and −0.99 for the seepage groundwater sample, 0.98 and −0.99 for the Dikrong river water sample and 0.99 and −0.99 for the mineral drinking water sample, respectively. These high correlation coefficients indicate excellent linearity, reproducibility and sensitivity of the sensor even in complex natural water matrices. The minimal interference from sample constituents confirms the robustness and practicality of the Ag–AuNPs system for on-site Co²⁺ detection in environmental monitoring.

Fig. 7 UV-visible absorption spectra of Ag–AuNPs upon the addition of a Co²⁺ solution to real samples and corresponding linear plots. (A) and (B) Seepage groundwater sample (SGW), (C) and (D) water sample from Dikrong river (DRW), and (E) and (F) mineral drinking water (MDW).

3.8 Possible mechanism

The hot aqueous garlic extract is reported to contain alliin as an active biocomponent.⁴² The possible reaction of the alliin with the Au and Ag particles is presented Fig. 8. All the structures in this figure were optimized. The Gibbs free energy of both the reactions was negative (−7.86 kcal mol⁻¹ and −8.19 kcal mol⁻¹ respectively). So, the reaction is favourable under optimum temperature and pressure.

Fig. 8 Reaction pathway of the synthesized Ag–Au NPs–Co complex.

In this reaction, the one Au reacts with the NH₂ group of the alliin and replacing one hydrogen. Subsequently, Ag reacts with Au resulting in the formation of Ag–Au NPs. Once the nanoparticles are formed, a cobalt (Co) atom is positioned between the two Ag–Au NPs, creating a complex. The oxygen atom from alliin bonds with the cobalt atom, serving as a bridge between the two Ag–Au NPs. The Co–O and Au–N bond lengths are 1.82 Å and 2.04 Å, respectively. The Ag atom connects with the Au atom in a triangular arrangement, with an Ag–Au bond length of 2.67 Å. The bonding interactions of Ag–N and Ag–Au in the Ag–Au NPs are analyzed using molecular orbital theory, as shown in Fig. 8. The HOMO−2 molecular orbital represents the Ag–Au bond, while the HOMO−4 molecular orbital corresponds to the Au–N bond (Fig. 9).

Fig. 9 The molecular orbitals of the Ag–Au NP complexes.

Similar bonding analysis was performed for the Co-contained Ag–Au NP complexes. In Fig. 10, the HOMO−2 represented the Co–O bond, and HOMO−3 and HOMO−7 occur due to the Ag and N bond. The bonding between Ag and Au is well described by the molecular orbital HOMO−5 and HOMO−9. The studied bonding analysis suggested that the Co containing complexes are well stabilized and the discussed reactions are favourable.

Fig. 10 The molecular orbitals of Ag–Au NPs–Co nanoparticles. The bond lengths are in Å.

4. Conclusion

A new colorimetric nanosensor, enabling highly sensitive and selective detection of Co²⁺, was developed through the synthesis of Ag–Au bimetallic nanoparticles with garlic-derived alliin. The sensor demonstrated remarkable specificity, with a detection limit of 0.09 nM, outperforming many conventional methods. The eco-friendly synthesis process and cost-effective approach make it a promising alternative for environmental and industrial applications. Reaction kinetics and molecular analysis confirmed the stability and efficiency of the Co²⁺–NP interaction. This innovative sensor offers a sustainable solution for monitoring cobalt contamination, contributing to enhanced public health, environmental safety, and advancements in metal ion detection technologies.

Author contributions

RP: conceptualization, investigation, analysis, and manuscript preparation. KT: validation, AKG: validation and software, MH: validation, CT: conceptualization, methodology, supervision, visualization, writing – review and editing. All read and approved the final version of the manuscript.

Conflicts of interest

The authors have no conflicts to declare. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be available on request.

Acknowledgements

The authors express their sincere appreciation to the Director, CSIR-NEIST (CSIR – North East Institute of Science and Technology) for the continuous support and valuable suggestions provided throughout the research. The author thanks the Analytical Chemistry Group & SAIF, CSIR-NEIST Jorhat for their instrumental support. The authors thank the SEED Division, DST New Delhi for financial support.

Notes and references

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