Tuning Co–Ni electrochemical active sites via iron incorporation in carbonate hydroxide frameworks for high-performance supercapacitors

Abstract

Transition metal incorporation is widely recognised as an efficient approach to enhance charge storage capability and ion transport behaviour in electrochemically active materials. In the present work, Fe-doped CoNi carbonate hydroxide (CoNi–CH) electrode materials were successfully synthesized using a hydrothermal technique, with controlled variation in host metal composition. The influence of Fe substitution on the structural, morphological, and electrochemical characteristics of CoNi–CH was systematically examined by introducing Fe into the Co and Ni lattice sites, yielding Fe_xCo_1−xNi–CH and Fe_xNi_1−xCo–CH systems, respectively. Structural analyses using X-ray diffraction, Raman spectroscopy, and FTIR confirmed that Fe substitution at the Co site leads to the emergence of a secondary FeOOH phase in the Fe_xCo_1−xNi–CH samples. In contrast, Fe incorporation at the Ni site resulted in single-phase CoNi–CH formation without detectable impurities. Morphological investigations revealed that Fe doping significantly alters the nanorod architecture, causing noticeable changes in diameter, surface texture, and overall morphology. The introduction of Fe not only modifies the defect structure and surface characteristics of CoNi–CH but also substantially improves its electrochemical performance. The optimized Fe_xCo_1−xNi–CH electrode delivered a specific capacitance of 1080 F g⁻¹ at a current density of 1 A g⁻¹, outperforming the undoped material due to the synergistic contribution of the FeOOH phase. More importantly, the optimized Fe_xNi_1−xCo–CH electrode exhibited an exceptionally high specific capacitance of 2898 F g⁻¹ at 1 A g⁻¹, demonstrating that Fe substitution at the Ni site is more effective in enhancing the charge storage performance of CoNi–CH materials. Overall, this work highlights the significance of site-selective transition-metal doping as an effective strategy for engineering high-performance supercapacitor electrode materials and offers valuable insights for future energy storage material design.

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Introduction

Energy storage has emerged as a key focus in current research. Among various storage devices, supercapacitors (SCs) represent a new class positioned between conventional capacitors and batteries.^1–3 They offer several advantages, including high power density, rapid charge–discharge capability, and extended cycle life.^4,5 However, their relatively low energy density remains a major limitation, requiring further investigation and advancement. Since the performance of SCs is strongly influenced by the properties of the electrode materials, optimising their structure and conductivity is considered a fundamental approach to enhance electrochemical efficiency.^6–8 In recent years, transition metal-centred carbonate hydroxides (TM-CHs) have attracted significant attention as promising electrode materials for SCs. Their crystal structure is similar to that of transition metal layered double hydroxides (LDHs), with carbonate ions (CO₃²⁻) positioned between the hydroxide layers.^9,10 The presence of CO₃²⁻ ions enhances the surface wettability of the electrode, thereby facilitating electrolyte penetration and improving ion transport during redox processes. As a result, TM-CHs exhibit excellent capacitive behaviour and often outperform oxide or hydroxide-based electrode materials.^11–14

Among many TM-CHs, nickel cobalt carbonate hydroxides (NiCo–CHs) received extensive attention from researchers as electrochemical active materials. Ni contributes a higher specific capacity, while the mixed valence state of Co provides enhanced chemical activity, together resulting in stable and efficient cycling performance. Nevertheless, the practical application of Ni–Co based electrode materials is constrained by inherent drawbacks, including poor intrinsic electronic conductivity, sluggish ion diffusion kinetics, and pronounced volumetric expansion during cycling. These adversely impact their energy storage performance and scalability in large-scale production. Compared to single-metal basic carbonates, bimetallic or polymetallic carbonates offer tunable chemical compositions, which allow better control over electrode structure design.^15–17 For example, Zhu et al. prepared nanosheet based microstructures of Ni₂(Co)₃OH₂, and reported a specific capacitance (C_sp) of 1178 F g⁻¹ at 1 A g⁻¹ with lower cycling stability.¹⁸ In another report, Sankar et al. prepared nanoflakes of Co₂(CO)₃OH₂ with a C_sp of 2111 mF cm⁻² with around 85% retention for 4000 cycles.¹⁹ A combination of these two in the form of NiCoCO₃(OH)₂ exhibited an improved C_sp of 1948 F g⁻¹ at 1 A g⁻¹ with 91.4% retention of the value up to 12 000 cycles.²⁰ To obtain further improvement in polymetallic systems, doping with Cu,^21,22 Zn,²³ Mn,²⁴ etc. represents an effective strategy to enhance the electrical conductivity and electrochemical activity of electrode materials by altering their charge states.²⁵ Consequently, polymetallic systems generally deliver superior electrochemical performance compared to their monometallic or bimetallic counterparts.^12,26 In this work we have chosen Fe as the dopant to expose abundant active sites of Ni and Co with high electron affinity to improve their electronic structure and accelerate the charge–discharge reaction in SCs.²⁶ Nevertheless, the effect of Fe-doping on the capacitive performance of NiCo–CHs has not been reported so far.

In this study, Fe-doped NiCo–CH (Fe_xNiCo–CH) nanoparticles were successfully synthesised via a one-step hydrothermal method with varying host element concentrations. In the first set of samples, we prepared Fe_xNi_1−xCo–CH composites, where the Ni content was adjusted in accordance with the Fe concentration. In another set, we prepared Fe_xNiCo_1−x–CH, to substitute the Fe dopant in the Co lattice site. In Fe_xNi_1−xCo–CH, we observed the impure phases of NiCo–CH along with FeOOH phases. Substitution doping of Fe in place of Ni in Fe_xNi_1−xCo–CH shows the presence of only NiCo–CH phases without any secondary peaks. The resulting nanorod morphology of the Fe_xNiCo–CH materials differs in the diameter of the nanorod structure that is observed in the undoped NiCo–CH. This suggests that Fe doping has significantly influenced the morphological evolution of the compound. The change in this doping condition leads to a noticeable change in structural and electrochemical results. To the best of our knowledge, there have been no comprehensive reports on the systematic variation of dopant Fe in Ni and Co-host sites in nanocrystals of NiCo–CHs. In this study, the synthesised materials were thoroughly investigated for their electrochemical characteristics. Moreover, the optimized Fe_xNiCo_1−x–CH electrode demonstrates a notably higher specific capacitance (1080 F g⁻¹ at 1 A g⁻¹) compared to its undoped counterpart due to the presence of FeOOH, whereas the optimized Fe_xNi_1−xCo–CH sample delivered a specific capacitance of 2898 F g⁻¹ at 1 A g⁻¹, confirming that substitution of Fe at Ni sites enhances the electrochemical performance of the NiCo–CH compound.

These findings highlight that even a small amount of Fe doping alters the elemental composition and morphology of the synthesised materials. This substantially boosts their electrochemical performance, offering a valuable strategy for the design of next-generation high-performance electrode materials. The novelty of this work lies in the site-selective Fe doping strategy in CoNi carbonate hydroxide (CoNi–CH) frameworks, where Fe was independently substituted at Co and Ni lattice sites to systematically investigate its influence on phase formation, morphology, and electrochemical performance. Unlike conventional compositional tuning studies, this work demonstrates that the dopant site critically determines the structural evolution and charge-storage behaviour. Fe substitution at the Co site induces a synergistic FeOOH secondary phase, while Fe incorporation at the Ni site stabilizes a pure single-phase structure with exceptionally enhanced capacitance (2898 F g⁻¹ at 1 A g⁻¹). This study provides a new insight into site-engineered transition-metal doping for developing high-performance supercapacitor electrodes.

Experimental methods

Chemicals

Nickel nitrate hexahydrate (Ni(NO₃)₂·6H₂O) (>99.9% purity), cobalt nitrate hexahydrate (Co(NO₃)₂·6H₂O) (>99.9% purity), iron nitrate nonahydrate (Fe(NO₃)₃·9H₂O), urea (CO(NH₂)₂) (>99.9% purity) and N-methyl-2-pyrrolidone (NMP) were all acquired from Sigma Aldrich. The supplier of ethanol was Merck. Nickel foam used was from Vritra technologies (>99% purity). Carbon black was acquired from MTI Corp.

Preparation of Fe_xNiCo–CH (x = 0, 0.05, 0.1, 0.2) electrodes

All chemical reagents were of analytical grade and were used without further purification. For the synthesis, Co(NO₃)₂·6H₂O, Ni(NO₃)₂·6H₂O and CO(NH₂)₂ were dissolved in the required amount of deionized water and stirred for several minutes to obtain a uniform solution (Fig. 1). This solution was then transferred into a 50 ml Teflon-lined stainless-steel autoclave for hydrothermal synthesis. The autoclave was sealed and was maintained at 160 °C for 06 hours.²⁷ During hydrothermal synthesis, the dissolved metal nitrates underwent complexation with urea which led to the formation of metal carbonate hydroxide. In the present synthesis process urea had served as a source of both carbonate and hydroxyl ions. The possible reaction can be stepped as follows;²⁸

NH₂CONH₂ + 3H₂O → 2NH₄⁺ + CO₂ + 2OH⁻ (1)

CO₂ + 2OH⁻ → CO₃²⁻ + H₂O (2)

2Ni²⁺ + CO₃²⁻ + 2OH⁻ → Ni₂CO₃(OH)₂ (3)

2Co²⁺ + CO₃²⁻ + 2OH⁻ → Co₂CO₃(OH)₂ (4)

Fig. 1 Schematic of Fe_xNiCo–CH nanoparticle synthesis and electrode preperation.

Using eqn (1)–(4), we get possible reactions and formation of the NiCo–CH material. After cooling to room temperature, the obtained nanoparticles were thoroughly washed with deionized water and absolute ethanol via centrifugation and dried at 75 °C for 4 hours. This washing step in water and ethanol for post-hydrothermal treatment was used and repeated two times to ensure the purity and stability of the final material. The synthesis of Fe_xNiCo_1−x–CH and Fe_xNi_1−xCo–CH was conducted using a precursor of Fe with a suitable concentration ratio variation of Ni(NO₃)₂·6H₂O and Co(NO₃)₂·6H₂O, respectively. In the next step, the conductive nickel foam (NF) substrate (1.5 cm × 1 cm) was pre-treated by sequential ultrasonic cleaning in 1 M hydrochloric acid (HCl), followed by ethanol, and deionised water to remove surface impurities and oxide layers. The cleaned NF was then dried at 75 °C overnight, and its mass was recorded. Separately, the electrode slurry was prepared by mixing the synthesized active material, polyvinylidene fluoride (PVDF) as a binder, and carbon black (Super P) as a conductive additive in a typical weight ratio of 80 : 10 : 10. A few drops of N-methyl-2-pyrrolidone (NMP) were added to the mixture to form a uniform and viscous slurry. The resulting slurry was then drop-cast onto the pre-treated nickel foam and evenly spread using a glass rod. The coated electrodes were dried in an oven at 60 °C for 12 hours to evaporate the solvent and ensure good adhesion of the active material to the current collector. Then the final mass of the electrode was noted to keep the electrode mass loading at about 1 mg cm⁻².²⁹

Material characterization

Structural analysis and phase identification were carried out by X-ray diffraction (XRD) using a Bruker D8 Advance ECO X-ray diffractometer with CuKα1 radiation (λ = 1.5404 Å) over a 2θ range of 10–60°. The present functional groups in the synthesized FeNiCo–CHs were characterised by using FTIR spectra using a Bruker 70 v with diamond ATR. The Raman spectra of the samples were recorded using a HORIBA Scientific, Lab RAMHR-UV-Open instrument with an excitation wavelength of 532 nm. The morphology and elemental distribution of the synthesized samples were examined using scanning electron microscopy (ZEISS Supra 55 model) and transmission electron microscopy (TEM, Talos F200X). High-resolution X-ray photoelectron spectroscopy (XPS [PHI 5000 versa probe III] using an Al K_α X-ray excitation source) was employed to determine the elemental composition and chemical states of the surface species.

Electrochemical measurements

Electrochemical performance was evaluated in a standard three-electrode setup using an Origaflex OGF 500 electrochemical workstation at room temperature with 1 M KOH aqueous solution as the electrolyte. The Ni foam-supported active material served as the working electrode, with platinum (Pt) and Hg/HgO as the counter and reference electrodes, respectively. The electrochemical tests included cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS), as well as cycling stability assessments. Before testing, to achieve stable electrochemical behaviour, the electrodes were preconditioned via continuous CV cycling at a scan rate of 50 mV s⁻¹ for fifty cycles.

Relevant electrochemical parameters, such as specific capacitance, were calculated using standard formulae as detailed below:²⁶

Specific capacitance from CV,

Specific capacitance from GCD,

C_sp(GCD) = I × Δt/(m × ΔV) (6)

where I (A) and V(V) are current and potential in the CV test, m (g) is the active mass loading, v is the scan rate (V s⁻¹), and ΔV(V) and Δt (s) are the potential window and discharging time in GCD, respectively.

Results and discussion

XRD

The XRD profiles of NiCo–CH samples (Fig. 2(a)) exhibit the characteristic reflections of a multimetallic hydroxide structure, consistent with the hydrotalcite-type NiCo carbonate hydroxide phase (JCPDS No. 38-0714, JCPDS no. 048-0083).^30,31 The distinct peaks at approximately 2θ = 17.27°, 24.10°, 26.57°, 30.73°, 33.9°, 35.2°, 36.5°, 39.64° and 44.5° correspond to the well-ordered NiCo–CH lattice. Upon Fe incorporation (replacing Co with Fe), small significant additional impurity peaks at 14.72°, 36.41° and 38.39° corresponding to the γ-FeOOH (lepidocrocite) phase (JCPDS No. 01-076-2301) have been detected, indicating that Fe³⁺ ions are not successfully integrated into the NiCo–CH lattice site by replacing Co²⁺ in the octahedral layers.^32,33 The appearance of a strong reflection near 14.7° in the Fe-rich samples (Fe_0.15Co_0.85Ni–CH and Fe_0.20Co_0.80Ni–CH) suggests the emergence of a strong γ-FeOOH phase,³⁴ resulting from high Fe content due to a chemical reaction,

Fe³⁺+ 3OH⁻→ Fe(OH)₃ → FeOOH + H₂O (7)

Fig. 2 XRD patterns of (a) Fe_xCo_1−xNi–CH and (b) Fe_xNi_1−xCo–CH samples.

The gradual shift of the reflection toward higher 2θ values indicates a slight decrease in interlayer spacing, attributed to charge compensation and lattice distortion introduced by Fe³⁺ and γ-FeOOH. Specifically, the diffraction peak located at 17.27° (2θ) in the undoped sample shifts to 17.39° (2θ) after incorporating FeOOH in Fe_xCo_1−xNi–CH, corresponding to a shift of approximately Δ(2θ) = 0.12° toward higher angles. The substitution of Fe³⁺ into the Co²⁺ site was not successful in Fe_xCo_1−xNi–CH as the ionic radius of Co²⁺ (0.745 Å, octahedral site) is more than that Fe³⁺ (0.645 Å, octahedral site). Conversely, the ionic radius of Ni²⁺ (0.69 Å, octahedral site) is reasonably close to that of Fe³⁺. Therefore, stoichiometric variation between Ni and Fe precursors resulted in a well-substituted Fe into the Fe_xNi_1−xCo–CH crystal site as observed in Fig. 2(b) and S1. The ionic radius mismatch between Co²⁺ and Fe³⁺ is therefore ∼0.10 Å, corresponding to ∼15.5% more relative to the Fe³⁺ radius, whereas the mismatch between Ni²⁺ and Fe³⁺ is ∼0.045 Å (∼7%). The substitutional solid solutions tend to remain stable when the ionic radius difference is below ∼10%, whereas larger mismatches can introduce significant lattice strain and promote phase segregation. In our case, the Co²⁺–Fe³⁺ mismatch (∼15%) exceeds this commonly reported tolerance limit, which can generate substantial local distortion and elastic strain energy in the lattice, favouring phase separation. In contrast, the Ni²⁺–Fe³⁺ mismatch (∼7%) falls within the range that can typically be accommodated within the host lattice, enabling the formation of a host-phase structure. Therefore, for these nanocrystals, the variation of Fe concentration in Fe_xNi_1−xCo CH does not lead to any additional peaks; it contains only peaks corresponding to NiCo–CH phases with a higher angle shift. The peak at 17.27°(NiCo–CH) shifted to 17.50° after Fe³⁺ ion incorporation in the Fe_xNi_1−xCo–CH sample, corresponding to a shift of approximately Δ(2θ) = 0.23°. This shift indicates a slight contraction in the lattice parameters, which can be attributed to the incorporation of Fe ions into the host lattice due to the difference in ionic radii between Fe ions and the host cations. A minimal additional peak arose corresponding to FeOOH for the Fe_0.2Ni_0.8Co–CH sample. These structural evolutions confirm that Fe incorporation in the Co site preserves the NiCo–CH framework along with FeOOH; conversely, Fe substitution in Ni sites formed only NiCo–CH without any other impurity. These results are consistent with the Raman and FTIR vibrational spectra that reveal Fe–O–M (M = Ni, Co) interactions and enhanced active surface chemistry in various ranges.³⁵

FTIR

The base sample, NiCo–CH shown in Fig. 3(a), exhibits characteristic FT-IR peaks corresponding to Ni/Co–OH and Ni/Co–OH vibrations at 523 and 1080 cm⁻¹, confirming the presence of metal–oxygen bonding in the hydroxide layers.^36,37 Additionally, bands observed at 1353, 832 and 705, 754, 657 cm⁻¹ are assigned to intercalate CO₃²⁻, respectively, within the hydroxide layers, consistent with previous reports on carbonate-containing LDHs.^38,39

Fig. 3 FTIR spectra of (a) Fe_xCo_1−xNi–CH and (b) Fe_xNi_1−xCo–CH samples.

A weak peak at 1510 cm⁻¹ is attributed to coordinated CO or CO²⁻ species likely resulting from interaction with interlayer anions.^11,40,41 The absorption bands near 2813 and 2924 cm⁻¹ correspond to the presence of a CH bond in the sample.^42,43 These peaks may arise due to the presence of urea as a precursor during synthesis which has been observed in earlier reports also, whereas a weak broad absorption band is found in the range of 3506–3748 cm⁻¹, along with a peak at 3398 cm⁻¹, assigned to O–H stretching from hydroxyl groups and hydrogen-bonded water, indicating a hydrated carbonated hydroxide structure.²⁸ With an Fe dopant, a significant change in adsorption peaks is observed at bands near 1020 cm⁻¹ and 766 cm⁻¹ corresponding to the bending vibration of –OH modes in γ-FeOOH.^44–46 These additional spectral changes were observed in a higher band range for Fe_xCo_1−xNi–CH samples. The peaks which emerged at 2813 and 2924 cm⁻¹, decrease with increasing Fe content, while a band at 3506–3748 cm⁻¹ increases with Fe content. This is attributed to the formation of FeOOH and the decrement of the carbonate structure in Fe_xCo_1−xNi–CH samples.⁴⁷ Furthermore, red shifts in the carbonate region (from 1353 to 1407 cm⁻¹) and in the bending vibration of adsorbed water or hydroxyls (from ∼3705 to 3505 cm⁻¹) suggest Fe-induced perturbations in the local coordination environment, possibly due to formation of Fe–O–M bridges or FeOOH-like surface species.^44,47 The shift of the carbonate band indicates changes in interlayer interactions, while the alteration in the OH region implies enhanced hydrogen bonding or modified surface hydroxyl configurations. These observations are consistent with Fe-induced surface reconstruction where Fe interacts with hydroxide and water species to form a FeOOH-enriched surface layer, especially at higher Fe loadings. Such transformations are commonly reported in multimetallic LDH systems undergoing partial oxidation or hydrothermal activation. This represents an insufficient substitution of Fe into Co sites of Fe_xCo_1−xNi–CH crystals. As shown in Fig. 3(b), for Fe_xNi_1−xCo–CH samples, the overall vibration patterns are the same in all samples. This represents the successful incorporation of Fe in the Ni site, which is in good agreement with XRD analysis. The peak intensity in the range of 800–1353 cm⁻¹ goes down with an increase in Fe concentration. This led to a decrease in the number of interlayer (CO₃²⁻) ions, which may have affected the electrochemical properties.

Raman spectroscopy

The Raman vibrational spectra of NiCo–CH in Fig. 4(a), can be divided into three characteristic regions. In the low-frequency region (100–500 cm⁻¹), bands are mainly attributed to metal–oxygen (M–O) stretching and bending vibrations within the brucite-like lattice. The peak at around 190 cm⁻¹ corresponds to F_2g modes of M–O bonding. The assignment of this peak is consistent with prior research.⁴⁸ The Co–O and Ni–O bonds contribute to peaks between ∼400 and ∼700 cm⁻¹, confirming the formation of a well-ordered Ni–O–Co framework.^49–51 Notably, this wide range contains peaks at 465 and 542 cm⁻¹ which are attributed to the collective vibrational influences of tetrahedral and octahedral oxygen atoms inside the crystal lattice.^48,52 In the mid-frequency region (∼1000–1400 cm⁻¹), the symmetric stretching vibration (ν₁) of interlayer carbonate ions (CO₃²⁻) is generally located at ∼1270–1400 cm⁻¹;⁵³ the exact position depends on the local coordination environment, including metal substitution and hydrogen-bonding with interlayer water molecules.

Fig. 4 Raman spectra of (a) Fe_xCo_1−xNi–CH and (b) Fe_xNi_1−xCo–CH samples.

In the high-frequency region (∼1400–1800 cm⁻¹), hydroxyl (OH⁻) stretching vibrations are observed. Sharp peaks between 1620 and 1703 cm⁻¹ have arisen from isolated hydroxyl groups (H–O–H) directly coordinated to Ni and Co, whereas broader bands in the same range are associated with hydrogen-bonded hydroxyls linked to interlayer water or carbonate species^33,54,55

Upon Fe incorporation for samples Fe_xCo_1−xNi–CH as shown in Fig. 4(b), Fe–O–Ni and Fe–O–Co linkages are created. The Fe–O stretching vibrations appear in the ∼500–1000 cm⁻¹ range, overlapping with existing Ni–O and Co–O modes, and lead to observable peak broadening. Furthermore, Fe incorporation often induces partial surface reconstruction during hydrothermal synthesis or post-treatment, resulting in the formation of a surface FeOOH layer, which enhances catalytic activity.³⁴ Raman spectroscopy of Fe_0.2Co_0.8Ni–CH has confirmed this transformation by exhibiting characteristic FeOOH wide bands corresponding to lepidocrocite (γ-FeOOH; ∼287, 333, 374, 528, 672 cm⁻¹).^34,56 The emergence of these bands demonstrates that Fe doping promotes surface FeOOH enrichment while preserving the underlying Ni–Co carbonate hydroxide lattice. Overall, the incorporation of Fe into NiCo(CO₃)₃OH introduces Fe–O–M (M = Ni, Co) linkages and facilitates surface reconstruction into FeOOH, while the Ni–O, Co–O, and (CO₃²⁻) vibrations remain characteristic of the nanostructure framework. The additional Fe–O and FeOOH vibrational features confirm the successful incorporation of Fe and the formation of a mixed-metal oxyhydroxide surface phase. However, a different trend was observed for the Fe_xNi_1−xCo-CH samples, where the incorporation of Fe did not introduce any additional vibrational bands compared to NiCo–CH. Similar to XRD and FTIR data, Raman data also revealed the successful substitution of Fe into NiCo–CH. Such an Fe-induced modification of the local coordination environment may promote synergistic redox interactions among Fe, Ni, and Co centres, thereby enhancing pseudocapacitive performance which can be analysed further with electrochemical measurements.

XPS

X-ray photoelectron spectroscopy (XPS) spectra of NiCo–CH, Fe_xNiCo_1−x–CH and Fe_xNi_1−xCo–CH were obtained to further investigate the effect of Fe incorporation and doping and valence states in NiCo–CH. For the NiCo–CH sample, in the Ni 2p spectrum (Fig. 5(a)), the main peaks at 873.1 and 855.3 eV are attributed to Ni 2p_1/2 and Ni 2p_3/2 orbitals, along with satellite peaks at 860.8 and 879.4 eV, respectively. The noted binding energy (BE) difference between main peaks is 17.8 eV, which resembles the Ni²⁺ phase. Furthermore, the deconvoluted high-resolution XPS spectrum of Co 2p (Fig. 5(b)) shows two prominent peaks observed at 780.8 eV (2p_3/2) and 796.5 eV (2p_1/2). The noted BE difference between the two prominent peaks is ∼16 eV, confirming the existence of Co²⁺.⁵⁷ Two shake-up satellite signals are also noted at 785.6 eV and 801.6 eV, supporting Co²⁺ formation. With Fe incorporation (in Fe_xNiCo_1−x–CH) the binding energies of Ni²⁺ and Co²⁺ are shifted positively by a small amount; conversely, in Fe_xNi_1−xCo–CH the shift is noticeably more positive. This shift indicated that Fe may have modulated the electron structure around Ni/Co to remain at a higher oxidation state.

Fig. 5 High-resolution XPS spectra of the as-prepared NiCo–CH, Fe_0.1Co_0.9Ni–CH and Fe_0.05Co_0.95Ni–CH materials. The narrow scan display (a) Ni 2p, (b) Co 2p, (c) O 1s and (d) Fe 2p regions.

In the analyzed C 1s spectra of NiCo–CH (Fig. S2), two distinct peaks are observed at binding energies of 284.8 eV and 286 eV, which are respectively attributed to C–C and C–O functional groups. However, for Fe_xNiCo_1−x–CH, the intensities of these peaks noticeably decrease, indicating a reduction in the presence of carbonate ions within the crystal structure. In contrast, the Fe_xNi_1−xCo–CH sample exhibits similar peaks to those observed for NiCo–CH. These observations are in good agreement with the FTIR analysis discussed above. In the O 1s region, peaks observed at 530.7 and 531 eV belong to the oxygen lattice, CO₃²⁻ and OH⁻, respectively.^26,58 With Fe incorporation (in Fe_xNiCo_1−x–CH) the binding energies of Ni²⁺ and Co²⁺ are shifted positively by a small amount; conversely, in Fe_xNi_1−xCo–CH the shift is noticeably more positive. This shift indicated that Fe may have modulated the electron structure around Ni/Co to remain at a higher oxidation state. A notable change is also observed for the O 1s region in Fig. 5(c), and the deconvoluted spectra of NiCo–CH show the appearance of two peaks at 530.7 and 532.0 eV which can be ascribed to CO₃²⁻ and OH⁻ species, respectively.²⁶ The presence of Fe in Fe_xNiCo_1−x–CH leads to the appearance of peak at 529.1 eV corresponding to lattice oxygen ascribed to the formation of the FeOOH bond, whereas with substitution doping of Fe in Fe_xNi_1−xCo–CH, the deconvoluted O 1s region represents CO₃²⁻ and OH⁻ species and a very small amount of lattice oxygen.

Additionally, all Fe-containing samples exhibit a prominent peak in the Fe 2p region, confirming the presence of Fe³⁺ cations. The deconvolution of the Fe 2p spectra shows main peaks at 710.5–712.5 eV and 721.3 eV, corresponding to the Fe 2p_3/2 and 2p_1/2 spin–orbit components of Fe³⁺, respectively. Satellite peaks are observed at approximately 715.5 eV and 727.3 eV,⁵⁹ which are characteristic of Fe³⁺ species. The presence of these satellite features further supports the oxidation state and local coordination environment of Fe within the carbonate-hydroxide lattice. These XPS results are consistent with the other structural analyses obtained from XRD, FTIR and Raman spectroscopy. Together, these complementary techniques confirm the successful incorporation of Fe³⁺ within the metal carbonate-hydroxide framework.

Morphology and elemental analysis

Fig. 6(a–i) shows the SEM images of the NiCo–CH, Fe_xNiCo_1−x–CH, and Fe_xNi_1−xCo–CH materials at different magnifications, as shown in Fig. S3. These images show that nanostructures grow a large number of independent nanorods. Fig. 6(a) shows that Ni–Co CH presents nano-urchin structures with smooth surfaces with a diameter of around 70 to 80 nm. The variation in Fe concentration in all the Fe_xNiCo_1−x–CH and Fe_xNi_1−xCo–CH samples shows a change in morphology and reveals an interlaced nanorod-like morphology with non-uniform length and a small diameter of around 30 to 40 nm and 50 to 60 nm, respectively. Fe precursors have altered the pH of the reaction solution due to partial hydrolysis of Fe salts in aqueous media, which may generate H⁺ ions. Such variations in pH can significantly influence the nucleation kinetics and growth rate of the nanostructures. Since anisotropic growth leading to nanorod formation is highly sensitive to the reaction environment, changes in pH may have modified the relative growth rates of different crystallographic planes, ultimately affecting the morphology and aspect ratio of the nanorods. Fe doping may influence the hydrolysis kinetics of the precursors. The presence of Fe ions can modify the rate at which metal ions hydrolyze and subsequently form oxide nuclei. Changes in hydrolysis rates can affect the number of nuclei formed during the early stage of the reaction, which in turn impacts the size, density, and assembly of the nanorods. Overall, these changes in pH and hydrolysis kinetics modify the nucleation and growth dynamics of the system, leading to variations in nanorod assembly, aggregation, and final architecture. The variation of pH values with variation in precursor and reaction conditions is included in Table S1. However, the TEM HRTEM images and SAED pattern show a clear indication of the change in morphologies as presented in Fig. 7. Fig. 7(a and b) show the presence of a nanorod shape in the NiCo–CH samples with an average diameter of 70 to 80 nm with various individual lengths. The high resolution TEM (HRTEM) image presented in Fig. 7(c) shows that the inter-planer spacing of ca. 0.62 nm is indexed to the XRD peak at 17.6° according to the standard card of Co(CO₃)_0.5(OH)·0.11H₂O (JCPDS 48-0083). The SAED patterns have further revealed the presence of highly crystalline domains within a polycrystalline framework of NiCo that is well-aligned throughout the entire nanorods along the [100] direction, which is similar to previous reports.^60,61 Incorporating Fe with varying Co concentrations reduced the nanorod size to 30–40 nm (Fig. 7(e and f)) and led to the formation of additional small nanoparticles (∼10–20 nm), as observed in the FESEM images. The presence of these additional particles which are γ-FeOOH phase was earlier supported by the characteristic peaks observed in the XRD and FTIR analyses. The HRTEM images in Fig. 7(g) present changes in crystallinity with Fe incorporation which is further confirmed by the SAED pattern in Fig. 7(h). The SAED patterns revealed that Fe incorporation has changed the crystallinity of NiCo–CH, from highly crystalline domains within a polycrystalline framework (Fig. 7(d)) to polycrystalline nature (Fig. 7(h)). Upon incorporation of Fe with variation in Ni concentration, i.e., Fe_0.05Ni_0.95Co–CH, the size of the nanorods was modified slightly with a diameter of around 50 to 60 nm, without any additional nanoparticles (Fig. 7(i and j)). When Fe is introduced at the Co site the crystallinity of NiCo–CH was reduced as per Fig. 7(k and l), which supports the XRD analysis results. The electrochemical measurements show clear evidence of variation in the electrochemical results with changes in Fe concentration and the host matrix, as discussed in further sections. Elemental composition and spatial distribution were further examined via Energy Dispersive X-ray Spectroscopy (EDS). The EDS results (Fig. S4) confirm the presence of Co, Ni, and Fe in expected ratios in all samples. We have performed ICP-AES analysis to quantitatively determine the actual content of each metal element in the electrode material. The obtained results confirm the successful incorporation of the constituent metals and are consistent with the intended stoichiometric composition of the synthesized sample. The detailed elemental concentrations are included in Table S2.

Fig. 6 SEM images of (a) NiCo–CH, (b–e) Fe_xCo_1−xNi–CH and (f–i) Fe_xNi_1−xCo–CH samples.

Fig. 7 TEM, HRTEM and SAED images of (a–d) the NiCo–CH sample, (e–h) Fe_0.1Co_0.9Ni–CH and (i–l) Fe_0.05Ni_0.95Co–CH.

The surface area and pore structure of the prepared samples were investigated using N₂ adsorption–desorption measurements at 77 K, and the corresponding isotherms are presented in Fig. 8(a–c). According to the IUPAC classification, all samples exhibit Type IV adsorption–desorption isotherms, which are characteristic of mesoporous materials. In the intermediate to high relative pressure range (P/P₀ = 0.5–1.0), a well-defined H3 hysteresis loop is seen suggesting the presence of rod-shaped mesopores created by the aggregation of interlayered rod-like particles. In the case of the NiCo–CH sample, the amount of nitrogen adsorbed increases gradually at low relative pressures, indicating the formation of monolayer followed by multilayer adsorption on the material surface. As the relative pressure approaches higher values, the adsorption increases more sharply. The specific surface area calculated for this sample is 27.2 m² g⁻¹, reflecting a moderately porous architecture. Upon Fe incorporation, a significant increase in surface area is observed. The Fe_0.1Co_0.9Ni–CH sample exhibits a BET surface area of 53.8 m² g⁻¹, suggesting that Fe doping modifies the structural arrangement and generates additional porous channels within the material.

Fig. 8 (a) CVs of all Fe_xCo_1−xNi–CH electrodes at a scan rate of 2 mV s⁻¹, (b) GCD graphs of all the electrodes at 1 A g⁻¹, (c) CVs of Fe_0.1Co_0.9Ni–CH at different scan rates, (d) GCD plot of Fe_0.1Co_0.9Ni–CH at various current densities from 1–15 A g⁻¹, (e) variation of specific capacitance with current densities, (f) relationship between log i vs. log V, (g) comparison of diffusion and capacitance contribution percentages at different scan rates of Fe_0.1Co_0.9Ni–CH, (h) determination of capacitance contribution of Fe_0.1Co_0.9Ni–CH from CV data at 2 mV s⁻¹ and (i) Nyquist profile of all Fe_xCo_1−xNi–CH samples.

The specific surface area in Fig. S5 of the sample Fe_0.05Ni_0.9Co–CH is the highest in comparison to others, i.e. 62.9 m² g⁻¹. This means that the surface area has increased even more with Ni replacement. The increased nitrogen absorption at elevated relative pressures signifies the existence of abundant mesopores and larger interparticle voids. The pore size distribution curves (insets of Fig. S5) show that the pores are mostly in the mesoporous region (2–50 nm) again. After adding Fe, the surface area and mesoporosity increase, which can make electroactive sites easier to reach and help the electrolyte get through more easily. These structural characteristics are beneficial for electrochemical energy storage applications, as they facilitate rapid ion diffusion and enhance redox kinetics during charge–discharge cycles.

Electrochemical results

Electrochemical tests were conducted using a three-electrode system. Fig. 8(a) and (b) show the CV contrast diagram of NiCo–CH and Fe_xCo_1−xNi–CH electrodes at 2 mV s⁻¹ and GCD contrast diagram at 1 A g⁻¹, respectively. It can be seen from Fig. 8(a) that the CV curve of each material shows an obvious redox peak indicating the existence of a Faraday reaction.⁶² Compared with NiCo–CH and all other Fe_xCo_1−xNi–CH, the CV closure curve area enclosed by Fe_0.1Co_0.9Ni–CH electrodes compared to other electrodes at the same scan rate is obviously larger. The GCD discharge time (in Fig. 8(b)) for Fe_0.1Co_0.9Ni–CH is longer compared to that of other electrodes at the same current density. This indicates that optimized Fe-incorporation in the Fe_xCo_1−xNi–CH electrode leads to a larger specific capacitance. The C_sp values of all the synthesised samples are listed in Table S3. Among the synthesised samples, Fe_0.1Co_0.9Ni–CH shows a high specific capacitance (C_s) value of 1080 F g⁻¹ at 1 A g⁻¹. At low Fe contents, the coexistence of FeOOH and NiCo–CH produces a synergistic effect that enhances charge-storage behaviour by providing additional redox-active sites and facilitating charge transfer. To understand the synergistic effect of each of the presented elements we have conducted GCD measurement for Ni_CH, Co_CH samples separately as mentioned in Fig. S6. These samples show a specific capacitance (C_s) value of 162 F g⁻¹ and 184 F g⁻¹, respectively at 1 A g⁻¹ which can be attributed to the synergistic interaction among the different metal species, leading to enhanced electrical conductivity, increased active sites, and improved charge-transfer kinetics. However, with increasing Fe content, the growth of a highly crystalline FeOOH phase becomes dominant. This phase exhibits lower electrical conductivity than its poorly crystalline counterpart, resulting in hindered electron transport and a consequent reduction in specific capacitance.⁶³ Fig. 8(c) and (d) show the CV and GCD curves of Fe_0.1Co_0.9Ni–CH under different input conditions. The same curves for all other electrodes at different scan rates (2, 5, 10, 20, 50, and 100 mV s⁻¹) and current densities (1, 2, 3, 5, 8, 10, and 15 A g⁻¹) are represented in Fig. S7 and S8 respectively. We can observe from Fig. 8(c) that with an increase in the scan rate, the position of the anodic peak shifts to a high potential due to polarization and current peak rise. As the scan rate gradually increases, the CV curves of all Fe_xCo_1−xNi–CH electrodes (Fig. 8(c) and S9) retain nearly the same shape. Moreover, the anodic peak shifts toward a more positive potential, while the cathodic peak shifts toward a more negative potential, indicating a rapid faradaic reaction occurring at the electrode–electrolyte interface. In addition, the GCD curves (in Fig. 8(d)) of the Fe_0.1NiCo_0.9–CH electrode and all other electrodes (in Fig. S10) show non-linear charge–discharge curves and no obvious IR drop at different current densities. The obvious redox peak and nonlinear charge–discharge curve indicate the energy storage nature of the pseudocapacitance electrode, which is consistent with the CV test results. The related redox energy storage reaction is described as follows in eqn (7)–(9). First, NiCo–CH interacts with hydroxyl ions to create metal oxyhydroxides (eqn (7)–(9)) followed by reversible transition of Ni²⁺ ↔ Ni³⁺ and Co²⁺ ↔ Co³⁺.

Ni₂CO₃(OH)₂ + 4OH⁻ → 2NiOOH + CO₃²⁻ + 2H₂O + 2e⁻ (8)

2Co(CO₃)_0.5(OH) + 4OH⁻ → 2CoOOH + CO₃²⁻ + 2H₂O + 2e⁻ (9)

The variation of specific capacitance of NiCo–CH and Fe_xCo_1−xNi–CH electrodes at different current densities is presented in Fig. 8(e). A reduction in C_sp has been found for NiCo–CH and Fe_xCo_1−xNi–CH, with an increase in current density from 1 to 15 A g⁻¹. At high current densities, electrolyte ions have limited time to penetrate the pores of the electrode and engage with the active sites, which restricts overall electrochemical performance.⁶⁴ Conversely, at lower current densities, ions have more time to access the porous structure of the electrode, allowing for better interaction with the active surface areas.⁶⁵

To understand the charge storage mechanism and reaction kinetics, CV analysis of all synthesized electrode materials is performed at low scan rates from 0.5 to 2 mV s⁻¹. In particular this analysis provides the percentage of ions that undergo the diffusion-controlled process and ion intercalation on the electrode. The overall effect of diffusive and capacitive behaviour of the synthesized electrode material is estimated by using the power law as represented by the equation,^66,67

i = aν^b (10)

log(i) = log(a) + b log(v) (11)

where i is the anodic or cathodic peak, v is the scan rate, and a and b are constants. The b value is determined from the slope of the linear region in the plot of log(i) versus log(ν), based on eqn (10) and (11). A b value of 1 indicates electric double-layer capacitor (EDLC) behaviour, characterized by surface-controlled charge storage. A b value of 0.5 corresponds to pseudocapacitive behaviour dominated by diffusion-controlled processes. Values between 0.5 and 1 suggest a mixed mechanism involving both surface and diffusion-controlled contributions, while a b value below 0.5 signifies battery-type behaviour, governed by bulk diffusion-controlled faradaic processes.^27,68

The b values of 0.45 and 0.41 were obtained from the anodic and cathodic peak currents of Fe_0.1Co_0.9Ni–CH, respectively, at low scan rates by linear fitting using the equation, as illustrated in Fig. 8(f). These values indicate a hybrid charge storage mechanism involving more diffusion-controlled faradaic contributions, thereby confirming the battery-type nature of the materials. The specific charge storage mechanism can be interpreted using the following equation:⁶⁹

i(V) = k₁v + k₂ν^1/2 (12)

In this expression, k₁v represents the surface-controlled (capacitive) contribution, while k₂ν^1/2corresponds to the diffusion-controlled (intercalation) contribution. For the Fe_0.1Co_0.9Ni–CH electrode, the proportion of diffusion capacitance contributions at scan rates of 2, 5, 10, 20, 50 and 100 mV s⁻¹ is calculated to be 89.6%, 84.5%, 79.4%, 73.2%, 53.3% and 54.9%, respectively (Fig. 8(g)). These results indicate that at higher scan rates, the overall capacitance is predominantly governed by surface-controlled processes. This trend is attributed to the shorter ion diffusion distances and limited accessibility to deeper active sites at elevated scan rates. As shown in Fig. 8(g), a slight increase in the diffusion-controlled contribution is observed with lower scan rates. This is likely due to the nanostructure providing ample open space for ion movement, enhanced exposure of electrochemically active sites, and improved interaction between electrolyte ions and the active material. However, at higher scan rates, the diffusion-controlled contribution declines again, as the fast charge–discharge dynamics favour reactions occurring primarily at the surface due to limited time for ion diffusion into the bulk material. Fig. 8(h) displays the capacitive contribution plotted against various potentials at a particular scan rate of 2 mV s⁻¹. Noticeably, the electrode Fe_0.1Co_0.9Ni–CH exhibits a capacitive contribution of approximately 10.3%, indicating that the diffusive mechanism is the predominant factor influencing the overall capacitance at a scan rate of 2 mV s⁻¹. The specific capacitance retention rate for the Fe_0.1Co_0.9Ni–CH sample is 51% after around 4000 cycles of charge discharge at 10 A g⁻¹ (Fig. S11).

Fig. 8(i) displays the electrochemical impedance spectroscopy (EIS) data and the corresponding equivalent circuit models for the Ni–Co precursor and Fe_xCo_1−xNi–CH electrodes, all measured under identical open-circuit voltage conditions and within a frequency range of 0.01 to 10³ Hz. From the fitted spectra across different frequency regions, it is evident that the NiCo precursor electrode exhibits relatively high values of charge transfer resistance (R_ct = 18.864 Ω). In contrast, the Fe_xCo_1−xNi–CH electrode shows significantly lower resistances, with R_ct reduced to 4.72 Ω, respectively. The details of all circuit fitting parameters of all the materials are presented in Table S3. Moreover, a steeper vertical line in the low-frequency region shows the occurrence of an improved diffusion phenomenon in all the Fe-doped samples compared to the former one.

However, compared to Fe_xCo_1−xNi–CH samples, the sample with Fe substitution in the Ni site in Fe_xNi_1−xCo–CH represents a different optimized concentration of the dopant for the highest C_sp. Fig. 9(a) illustrates the largest CV curve along with the longest discharge time for Fe_0.05Ni_0.95Co–CH compared to NiCo–CH and other samples in the set Fe_xNi_1−xCo–CH. As the scan rate gradually increases, the CV curves of the Fe_xNiCo_1−x–CH electrodes retain nearly the same shape. The peak current is influenced by the scan rate, and thus oxidation and reduction potential shifted to positive and negative potentials with an increase in the scan rate, respectively. The GCD profile for Fe_xNi_1−xCo–CH with various Fe concentrations is shown in Fig. 9(b). The C_sp values of all the synthesised samples are listed in Table S3. The sample Fe_0.05-Ni_0.95-Co CH exhibits a C_sp of 2898 F g⁻¹ at 1 A g⁻¹, which is the highest compared to that of other sets of samples presented. The observed specific capacitance values have been further compared with reported articles as mentioned in Table S4.^11,70–75 The optimum amount of Fe³⁺ ion substitution at the Ni²⁺ site leads to enhancement in active surface area and conductivity, which leads to the highest C_sp value. With an increase in Fe concentration, a decrease in interlayered nitrates and carbonate ions may lead to the reduced diffusion properties and C_sp values. This claim has been formerly verified with FTIR analysis. Fig. 9(c) and (d) present the variation in CV and GCD of Fe_0.05Ni_0.95-Co CH at the various mentioned scan rates and current densities, respectively. Furthermore, with an increase in current density and scan rates, the specific capacitance of all samples shows a decrease, with a change from its initial value (Fig. 9(e) and S9–S10). The b-value of 0.8 for Fe_0.05Ni_0.95Co–CH suggests battery-like behaviour with a combined pseudo-capacitance mechanism as presented in Fig. 9(f). On the other hand, the b-values for Fe_0.1Ni_0.9Co–CH, Fe_0.15Ni_0.85Co–CH and Fe_0.2Ni_0.8Co–CH are about 0.5 (Table S3), indicating that combined diffusion capacitance and pseudo-capacitance contributions have been reduced. This result supports the claim of the reason for the decrease in C_sp value at other concentrations. In comparison to Fe_0.1NiCo_0.9–CH, the Fe_0.05Ni_0.95Co–CH electrode demonstrates reduced surface capacitance contributions along with increased diffusion capacitances of- 90.1%, 85.2%, 80.3%, 74.3%, 64.6% and 56.4% at scan rates of 2, 5, 10, 20, 50, and 100 mV s⁻¹, respectively (as shown in Fig. 9(g)). This behaviour suggests that the energy storage mechanism in the Fe_0.05Ni_0.95Co–CH electrode is almost entirely dominated by surface-controlled capacitance at high scan rates (Fig. 9(h)). The specific capacitance retention rate for the Fe_0.05Ni_0.85Co–CH sample is 63% after around 4000 cycles of charge discharge at 10 A g⁻¹ (Fig. S11) and represents better rate capability compared to Fe_0.1Co_0.9Ni–CH. As shown in Fig. 9(i) it is evident that the Fe_xNi_1−xCo–CH electrode shows significantly lower resistances, with R_ct reduced to 0.76 Ω, respectively. The details of all circuit fitting parameters of all the materials are presented in Table S3. These findings confirm that incorporating an optimized amount of Fe into the Ni–Co matrix improved the electrical conductivity of the resulting ternary electrode material and facilitated low ion transfer resistance during the energy storage process.

XRD (Fig. S12(a)) and SEM (Fig. S12(b) and (c)) characterization studies of the recycled active materials have been added to further confirm the structural integrity and stability of the electrode after recycling. The post-stability XRD results confirm the retention of the NiCo carbonate hydroxide phase, although a reduction in peak intensity is observed after electrochemical cycling. The post-stability testing SEM images reveal that the nanoparticle morphology is largely preserved (Fig. 7(a)–(d)); however, increased surface agglomeration is observed compared to the as-synthesized nanoparticles.

Fig. 9 (a) CVs of all Fe_xNi_1−xCo–CH electrodes at a scan rate of 2 mV s⁻¹, (b) GCD graphs of all the electrodes at 1 A g⁻¹, (c) CVs of Fe_xNi_1−xCo–CH at different scan rates, (d) GCD plot of Fe_xNi_1−xCo–CH at various current densities from 1–15 A g⁻¹, (e) variation of specific capacitance with current densities, (f) relationship between log i vs. log V, (g) comparison of diffusion and capacitance contribution percentages at different scan rates of Fe_0.05Ni_0.95Co–CH, (h) determination of the capacitance contribution of Fe_0.05Ni_0.95Co–CH from CV data at 2 mV s⁻¹ and (i) Nyquist profile of all Fe_xCo_1−xNi–CH samples.

Conclusion

In conclusion, nanorod-shaped FeNiCo–CH electrodes were successfully synthesised via a one-step hydrothermal approach. Controlled Fe incorporation effectively modulated the crystal structure, morphology, and electronic conductivity of the NiCo–CH system. Fe substitution at the Ni site enabled single-phase NiCo–CH formation and superior electrochemical performance, whereas Fe substitution at the Co site led to secondary FeOOH phase formation. Initially, the synergistic formation of FeOOH with Fe_xCo_1−xNi–CH enhances the charge-storage performance. However, as the highly crystalline FeOOH phase becomes dominant, the specific capacitance decreases, owing to the lower electrical conductivity of highly crystalline FeOOH compared to its low-crystallinity counterpart. Although an increase in FeOOH crystallinity can reduce defect density and limit the number of electrochemically active sites, which may negatively influence electronic transport, other factors may also contribute to the gradual performance decay.^76,77 On the other hand, in Fe_xNi_1−xCo–CH, an optimal level of Fe³⁺ substitution at the Ni²⁺ site enhances the active surface area and electrical conductivity, resulting in the highest specific capacitance. Further increases in Fe concentration reduce the content of interlayered nitrate and carbonate ions, which impairs ion diffusion and consequently lowers the specific capacitance. The optimized Fe_0.05Ni_0.95Co–CH electrode exhibited the highest specific capacitance of 2898 F g⁻¹ at 1 A g⁻¹, demonstrating the critical role of Fe doping in enhancing charge transport and ion diffusion with a rate capability of around 63% at 4000 cycles at 10 A g⁻¹. During extended charge–discharge cycling, structural evolution of the FeOOH phase may occur, potentially affecting the stability of the active material. In addition, repeated cycling can promote particle agglomeration, which reduces the accessible surface area and limits electrolyte penetration. The gradual development of higher interfacial resistance at the electrode–electrolyte interface during long-term cycling may further restrict charge transfer and ion diffusion. These findings provide valuable insight into compositional engineering strategies for the design of high-performance electrode materials for advanced energy storage applications.

Conflicts of interest

The authors declare no competing financial interest.

Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary information (SI). Raw data that support the findings of this study are available from the corresponding author upon reasonable request. Supplementary information is available. See DOI: https://doi.org/10.1039/d6ta01010e.

Acknowledgements

D.M. acknowledges C. V. Raman Global University, Bhubaneswar, Odisha, India, for providing doctoral fellowship and XRD characterization facility available at Central Research Facility of the University. Additionally, M. C. acknowledges the Council of Scientific and Industrial Research, Human Resource Development Centre (CSIR-HRDG) for the financial support with project No. 03WS (001)/2023-24/EMR-II/ASPIRE.

References

1. J. Libich, J. Máca, J. Vondrák, O. Čech and M. Sedlaříková, J. Energy Storage, 2018, 17, 224–227.
2. N. Parvin, D. Merum, M. Kang, S. W. Joo, J. H. Jung and T. K. Mandal, J. Mater. Chem. A, 2025, 13, 24320–24386.
3. F. Mahmood, M. Ali, M. Khan, C. F. M. Mbeugang, Y. M. Isa, A. Kozlov, M. Penzik, X. Xie, H. Yang, S. Zhang and B. Li, Ind. Crops Prod., 2025, 227, 120830.
4. S. V. Bhosale and S. V. Bhosale, Chem. Sci., 2025, 16, 10159–10227.
5. J. Meena, S. shankari Sivasubramaniam, E. David and S. K, RSC Sustain., 2024, 2, 1224–1245.
6. M. M. Ram, R. Sapna, S. R. Rondiya and K. Hareesh, J. Mater. Chem. A, 2025, 13, 40448–40489.
7. K. V. G. Raghavendra, R. Vinoth, K. Zeb, C. V. V. Muralee Gopi, S. Sambasivam, M. R. Kummara, I. M. Obaidat and H. J. Kim, J. Energy Storage, 2020, 31, 101652.
8. T. T. Mishra, M. Chakraborty, J. N. Behera and D. Roy, Energy Fuels, 2024, 38, 9186–9217.
9. L. Mao, X. Zhao, Y. Li and L. Chen, J. Colloid Interface Sci., 2022, 624, 482–493.
10. Y. Wang, D. Jiang, Y. Zhang, J. Chen, M. Xie, C. Du and L. Wan, J. Colloid Interface Sci., 2024, 654, 379–389.
11. S. Kumar, B. K. Satpathy and D. Pradhan, Mater. Adv., 2024, 5, 2271–2284.
12. E. Vivek, J. Kumar, Y. J. Kwon, H. J. Jung, R. R. Neiber, K. Zhang, G. M. Choi, J. Park, M. J. Lee, S. H. Kim, K.-J. Baeg and K. Y. Cho, J. Energy Storage, 2024, 103, 114472.
13. Q. Wu, Y. Zhong, R. Chen, G. Ling, X. Wang, Y. Shen and C. Hao, Ind. Crops Prod., 2024, 222, 119676.
14. S. Kumar and D. Pradhan, J. Mater. Chem. A, 2025, 13, 23470–23486.
15. X.-L. Wang, G.-F. Zhang, R. Nasser, T.-T. Jiang, Q.-W. Cao, M. Gong, X.-Y. Li and J.-M. Song, J. Alloys Compd., 2022, 906, 164270.
16. L. Liu and A. Corma, Chem. Rev., 2023, 123, 4855–4933.
17. S. Ding, D. Tong, X. Pan and L. Zhao, J. Alloys Compd., 2025, 1010, 177985.
18. G. Zhu, C. Xi, M. Shen, C. Bao and J. Zhu, ACS Appl. Mater. Interfaces, 2014, 6, 17208–17214.
19. K. V. Sankar, Y. Seo, S. C. Lee, S. Liu, A. Kundu, C. Ray and S. C. Jun, Electrochim. Acta, 2018, 259, 1037–1044.
20. P. Sivakumar, M. Jana, P. Nakhanivej, M. G. Jung, C. J. Raj and H. S. Park, Int. J. Energy Res., 2021, 45, 3064–3074.
21. Z. Zhang, Y. Cheng, H. Li, Z. Xu, S. Sun and S. Yin, ACS Appl. Energy Mater., 2022, 5, 6642–6653.
22. M. Zhang, X. Liu, M. Ma, G. Ni, Z. Sun, J. Liu and Y. Wu, Energy Fuels, 2021, 35, 20358–20366.
23. Y. Miao, T. Wang, J. Hua, K. Liu, Z. Hu, Q. Li, M. Zhang, Y. Zhang, S. Liu, X. Xue, J. Qi, F. Wei, Q. Meng, Y. Ren, B. Xiao, Y. Sui and P. Cao, ACS Appl. Mater. Interfaces, 2021, 13, 39205–39214.
24. X. Cao, Y. Liu, Y. Zhong, L. Cui, A. Zhang, J. M. Razal, W. Yang and J. Liu, J. Mater. Chem. A, 2020, 8, 1837–1848.
25. Y. Tan, X. Niu and Y. Ren, J. Energy Storage, 2024, 95, 112619.
26. H. Zhang, S. Liu, E. Hu, Y. Yang, H. Zhang, Y. Zhu, L. Yan, X. Gao, J. Zhang and Z. Lin, Carbon Energy, 2022, 4, 939–949.
27. M. Chakraborty, H. R. Inta, A. Roy, H. V. S. R. M. Koppsetti, S. Ghosh, N. Bhandary, D. Roy, S. Debnath and V. Mahalingam, Sustain. Energy Fuels, 2025, 9, 4337–4351.
28. M. Bhatt, B. Gupta and A. K. Sinha, Sci. Rep., 2025, 15, 2192.
29. M. Sethi, U. S. Shenoy and D. K. Bhat, Nanoscale Adv., 2020, 2, 4229–4241.
30. C.-C. Tseng, J.-L. Lee, Y.-M. Liu, M.-D. Ger and Y.-Y. Shu, J. Taiwan Inst. Chem. Eng., 2013, 44, 415–419.
31. T. Zhou, W. Gao, Q. Wang and A. Umar, Materials, 2018, 11, 207.
32. D. Zhou, P. Li, X. Lin, A. McKinley, Y. Kuang, W. Liu, W.-F. Lin, X. Sun and X. Duan, Chem. Soc. Rev., 2021, 50, 8790–8817.
33. A. Mavrič and M. Valant, Inorganics, 2023, 11, 296.
34. M. Hanesch, Geophys. J. Int., 2009, 177, 941–948.
35. X. Fu, J. Yu, H. Li, Y. Zhang, S. Pan, Y. Fu and B. Li, J. Power Sources, 2025, 630, 236159.
36. Y. Ding, L. Xu, C. Chen, X. Shen and S. L. Suib, J. Phys. Chem. C, 2008, 112, 8177–8183.
37. V. Gupta, S. Gupta and N. Miura, J. Power Sources, 2008, 175, 680–685.
38. R. Li, Z. Hu, X. Shao, P. Cheng, S. Li, W. Yu, W. Lin and D. Yuan, Sci. Rep., 2016, 6, 18737.
39. S. Xiu, H. Jung, B. Quan and C. J. An, Int. J. Energy Res., 2022, 46, 19667–19677.
40. H. Du, C. T. Williams, A. D. Ebner and J. A. Ritter, Chem. Mater., 2010, 22, 3519–3526.
41. M. Hamid, M. Rianna, M. D. E. Vania, I. D. Yanti, F. A. A. Manurung, R. Afriandani and A. Daulay, Mater. Sci. Energy Technol., 2023, 6, 382–387.
42. S. Liu, C. An, X. Chang, H. Guo, L. Zang, Y. Wang, H. Yuan and L. Jiao, J. Mater. Sci., 2018, 53, 2658–2668.
43. S. Mangrio, A. Tahira, I. A. Mahar, M. Parveen, A. A. Hullio, D. A. Solangi, A. Khawaja, M. A. Bhatti, Z. A. Ibupoto, A. B. Mallah, A. Nafady, E. A. Dawi, A. Al Karim Haj Ismail, M. Emo, B. Vigolo and Z. H. Ibupoto, J. Nanopart. Res., 2023, 25, 195.
44. S. Rahimi, R. M. Moattari, L. Rajabi, A. A. Derakhshan and M. Keyhani, J. Ind. Eng. Chem., 2015, 23, 33–43.
45. M. Amini, Y. Mousazade, Z. Zand, M. Bagherzadeh and M. M. Najafpour, Sci. Rep., 2021, 11, 6642.
46. Y. Lin, Y. Wei and Y. Sun, J. Mol. Catal. A: Chem., 2012, 353–354, 67–73.
47. J. Fan, Z. Zhao, Z. Ding and J. Liu, RSC Adv., 2018, 8, 7269–7279.
48. P. Bhojane and P. M. Shirage, Appl. Surf. Sci., 2023, 615, 156352.
49. P. Kumar, V. Kumar and V. Dhiman, Sustain. Chem. Clim. Action, 2025, 7, 100110.
50. S. K. Konavarapu, G. Kim, K. Shin and S. Y. Kim, Chem.–Eur. J., 2025, 31, e202500010.
51. M. S. Vidhya, G. Ravi, R. Yuvakkumar, D. Velauthapillai, M. Thambidurai, C. Dang and B. Saravanakumar, RSC Adv., 2020, 10, 19410–19418.
52. A. Safdar, H. E. A. Mohamed, A. Muhaymin, K. Hkiri, N. Matinise and M. Maaza, Sci. Rep., 2024, 14, 17620.
53. C. Croitoru, A. Pascu, I. C. Roata and E. M. Stanciu, IOP Conf. Ser.: Mater. Sci. Eng., 2017, 209, 012041.
54. C. Hobbs, S. Jaskaniec, E. K. McCarthy, C. Downing, K. Opelt, K. Güth, A. Shmeliov, M. C. D. Mourad, K. Mandel and V. Nicolosi, npj 2D Mater. Appl., 2018, 2, 4.
55. Q. Tao, B. J. Reddy, H. He, R. L. Frost, P. Yuan and J. Zhu, Mater. Chem. Phys., 2008, 112, 869–875.
56. R. Barik, B. K. Jena and M. Mohapatra, RSC Adv., 2017, 7, 49083–49090.
57. T. T. Mishra, M. Sadangi, J. N. Behera, M. Chakraborty and D. Roy, Sustain. Energy Fuels, 2026, 10, 364–374.
58. J. Stoch and J. Gablankowska-Kukucz, Surf. Interface Anal., 1991, 17, 165–167.
59. Y. Hu, W. Gu, H. Hu, X. Li and Q. Zhang, Environ. Sci. Pollut. Res., 2022, 29, 27421–27429.
60. N. Mahmood, M. Tahir, A. Mahmood, J. Zhu, C. Cao and Y. Hou, Nano Energy, 2015, 11, 267–276.
61. J. Xiao, X. Zeng, W. Chen, F. Xiao and S. Wang, Chem. Commun., 2013, 49, 11734.
62. K. Song, J. Li, T. Abdiryim, R. Jamal, H. Yang, Y. Zhou, Z. Wang, G. Zhang, J. Liu, W. Zhang and J. Chen, Adv. Funct. Mater., 2025, 35, 2505998.
63. G. Pandey, P. W. Menezes and K. Awasthi, Adv. Energy Sustain. Res., 2025, 6, 2500011.
64. Z. Zhu, Y. Liu, H. Zhang, Y. Xu, Z. Shao, L. Ge, Z. Wang and H. Chen, Energy Rev., 2025, 4, 100118.
65. T. Kress, X. Liu and A. C. Forse, Nat. Mater., 2026, 25, 440–446.
66. T. T. Mishra, M. Chakraborty, C. K. Parashar, P. S. G. Pattader, F. Mayanglambam and D. Roy, ChemNanoMat, 2025, 11(5), e202400635.
67. S. Ghosh, H. R. Inta, M. Chakraborty, G. Tudu, H. V. S. R. M. Koppisetti, K. S. Paliwal, D. Saha and V. Mahalingam, ACS Appl. Nano Mater., 2022, 5, 7246–7258.
68. H. V. S. R. M. Koppisetti, H. R. Inta, K. Mukhuti, A. Roy, S. Ghosh, V. G. Pol and V. Mahalingam, J. Energy Storage, 2024, 86, 111178.
69. T. T. Mishra, M. Chakraborty, C. K. Parashar, J. Mahanta, P. S. G. Pattader and D. Roy, J. Mater. Sci.: Mater. Electron., 2025, 39(35), 16715–16736.
70. H. Pourfarzad, M. Shabani-Nooshabadi, M. R. Ganjali and H. Kashani, Electrochim. Acta, 2019, 317, 83–92.
71. H. Li, X. Zhou and P. Zhao, Nanotechnology, 2023, 34, 065703.
72. L. Jiang, Y. Sui, J. Qi, Y. Chang, Y. He, Q. Meng, F. Wei, Z. Sun and Y. Jin, Part. Part. Syst. Char., 2017, 34, 1600239.
73. H. Lim, Y. S. Park and S. Baek, J. Am. Ceram. Soc., 2025, 108, e70061.
74. F. Wang, S. Sun, Y. Xu, T. Wang, R. Yu and H. Li, Sci. Rep., 2017, 7, 4707.
75. S. Kumar and D. Pradhan, ACS Appl. Energy Mater., 2025, 8, 9878–9890.
76. J. Liu, M. Zheng, X. Shi, H. Zeng and H. Xia, Adv. Funct. Mater., 2016, 26(6), 919–930.
77. J. Chen, J. Xu, S. Zhou, N. Zhao and C.-P. Wong, Nano Energy, 2016, 21, 145–153.

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