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Characterization of EFM adsorbentBET analysisThe surface properties of newly prepared adsorbent and its components were investigated through nitrogen adsorption–desorption isotherms. Table 1 presents the results of the adsorbent the textural properties assessment.Table 1 EFM adsorbent and raw materials used (magnetite, eggshell and fly ash)—specific surface area determinate by Brunauer–Emmett–Teller theory (BET).Full size tableFrom the quantitative data reported in Table 1 it can be observed that eggshell BET/N2 surface area is 0.67 m2/g, value similar to that in the literature15,31,40.According to Table 1 the BET/N2 specific surface area for fly ash, is 4.961 m2/g. Apparently the value obtained in this study seems higher than in the data reported in the literature (0.414 m2/g). However, it should be noted that both the type of ash used and the experimental conditions of this study differ from the data reported in the literature16,22.As expected, very different values were obtained for the specific surface of M1 (25.196 m2/g) and respectively 26.866 m2/g for M2, which correspond to the adsorbent prepared in the two molar ratios studied. This difference can be justified by the variation of the ratio between eggshell and fly ash in M1 and M2, respectively.Physical properties of adsorbent (pore size, pore volume) were investigated using the low-temperature (77 K) nitrogen adsorption–desorption isotherms. As presented in Fig. 1 the isotherms of EFM fitted in a type II isotherm with a H3 hysteresis loop, indicating a macroporous structure for the both adsorbent molar ratio (M1 and M2)38.Figure 1The nitrogen adsorption–desorption isotherms for EFM adsorbent.Full size imageXRD studiesThe mineralogical compositions of the raw materials as well as of the adsorbent were studied through XRD analysis. The size of the crystalline domains was evaluated by means of the Debye–Scherrer formula (Eq. 14)41,$$D = frac{0.89lambda }{{beta cos left( theta right)}}$$
(14)
where (lambda) is the X-ray wavelength of Cu K-α ((lambda = 0.15406;{text{nm}})), (beta) is the full width at half maximum in radians and (theta) is the Bragg angle.From the most prominent peak, one gets D = 21.6 nm.The XRD spectrum of magnetite sample (Fig. 2) shows the diffraction peaks of the well crystallized spinel phase magnetite Fe3O4 (COD 9005837) with average crystallite size of 21.6 nm. In the XRD spectrum of eggshell sample (Fig. 3) are recorded the diffraction peaks of the single phase well crystallized calcite CaCO3 (COD 9000965) with mean crystallite size of 125.8 nm15,40.Figure 2XRD spectra of magnetite.Full size imageFigure 3XRD spectra of eggshell.Full size imageThe XRD spectrum (Fig. 4) shows that the fly ash sample has a complex composition. Four crystalline phases have been identified whose characteristics are presented in Table 2.Figure 4XRD spectra of fly ash sample.Full size imageTable 2 The phase compositions of fly ash sample.Full size tableFrom the data presented in the Table 2 indicates that fly ash sample used in the preparation of the adsorbent is not non-hazardous solid waste22.In the XRD spectra of M1 (Fig. 5a) are visible the diffraction peaks characteristic of the crystalline phases existing in the eggshell (calcite CaCO3), the fly ash (but only those of the phase with more intense peaks, quartz SiO2), and magnetite (Fe3O4). Because the eggshell is mixed in a larger proportion than the other two components, the CaCO3 peaks are the most intense.Figure 5(a) XRD spectra of M1. (b) XRD spectra of M2. (c) XRD spectra of magnetite, eggshell, fly ash, M1 and M2.Full size imageAnalyzing the XRD spectrum obtained for M2 (Fig. 5b), the crystalline phases that can be identified are: magnetite Fe3O4, calcite CaCO3, quartz SiO2, and corundum Al2O3. The most intense are the magnetite peaks. In this sample, because the ash is mixed in a larger proportion, the SiO2 peaks are more intense and another crystalline phase present in it (Al2O3) becomes visible in the spectrum.Figure 5c shows the overlapping XRD spectra of M1, M2 and raw materials (magnetite, fly ash and eggshell). In both M1 and M2, the phase peaks of individual components can be observed.However, due to the different materials crystallinity, only the most intense peaks appear by overlap. Also, the phase diffraction lines (Figs. 2, 3, 4, 5) are no longer visible.SEM micrographsThe surface morphology and particle size of raw materials and adsorbent were investigated through SEM technique. The micrographs are presented in Figs. 6, 7, 9, 10, 11, 12, 13 and 14.Figure 6Two-dimensional image of the magnetite particle obtained by the SEM technique.Full size imageFigure 7Two-dimensional image of the ash fly particle obtained by the SEM technique.Full size imageThe SEM image of magnetite (Fig. 6) suggest that particles are of nanometric dimensions (with the average size about 21 nm), uniform and with a cubic structure25,42,43.Figure 7 shows that the fly ash particles are of porous spherical shapes with different sizes as well as porous irregularly or angularly shaped particles16,44. As can be seen in (Fig. 7) the surface of the ash sphere is irregular and has streaks due to the mechanical and thermal stress.Figure 8 presents the elemental composition of the ash fly determined through EDX analysis.Figure 8EDS spectra of fly ash sample.Full size imageAccording to the data from EDX (Fig. 8), there are only seven elements which are predominant in sample: aluminum, iron, magnesium, calcium, silica, oxygen and sulphur16,45.SEM micrograph of eggshell sample (Fig. 9a,b) indicates a different size (about 100 nm) irregular crystal on multihole surface structure36,40.Figure 9(a) Two-dimensional image of the eggshell particle obtained by the SEM technique. (b) Two-dimensional image of the eggshell particle obtained by the SEM technique.Full size imageThe morphology of M1 (Fig. 10a,b) indicates the presence of agglomerations of particles of different sizes in the nano field, spherical shape, cubic shaped and irregular crystal structure sizes, suggesting a good connectivity between them.Figure 10(a) Two-dimensional image of M1 particle obtained by the SEM technique (magnitude 3 µm). (b) Two-dimensional image of M1 particle obtained by the SEM technique (magnitude 5 µm).Full size imageAlso, the (Fig. 10b) indicates that the cubic-shaped particles characteristic of magnetite (Fig. 6) loaded into the pores of the ash and eggshell particles.The Fig. 11 shows the live map for M1 and the distribution of the identified elements.Figure 11SEM M1- Live map.Full size imageThe SEM micrograph of M2 (Fig. 12a,b) the same agglomerations of particles of different nano-sizes, spherical shape, cubic shaped and irregular crystal structure sizes are observed as in the case of SEM graph for M1.Figure 12(a) Two-dimensional image of M2 particle obtained by the SEM technique (magnitude 3 µm). (b) Two-dimensional image of M2 particle obtained by the SEM technique (magnitude 5 µm).Full size imageThe Fig. 13 shows the live map for M2 and the distribution of the identified elements.Figure 13M2 SEM—Live map.Full size imageThe comparative analysis of the Fig. 13 showing Live map for M2 and M1 (Fig. 11) highlights the presence of differences regarding the proportion of identification elements in the two samples, due to the different molar ratio between eggshell and ash.In the Fig. 14a can be observed a larger number of spherical particles characteristic of district heating ash, as a result of the change in the ratio between the two wastes (eggshell:ash = 3:1), loaded with magnetite particles. At the same time, in SEM the micrograph for M1 (Fig. 14b) is much more obvious the multihole structure of the eggshell.Figure 14(a) Two-dimensional image of M1 particle obtained by the SEM technique (magnitude 30 µm). (b) Two-dimensional image of M2 particle obtained by the SEM technique (magnitude 50 µm).Full size imageThe analysis of the SEM micrograph (Fig. 14a) of the M1 sample (in which the eggshell component is predominant) indicates that the multi porous structure of the eggshell is loaded with both the cubic-shaped particles of the magnetite and the spherical ones belonging to the ash sample. This aspect is much more visible in the case of SEM micrograph of the M2 sample (Fig. 14b), considering the fact that in this ash is found in the majority proportion (magnetite:eggshell:ash = 1:1:3).This result suggests that through the procedure of mechanical alloying in the mill with high energy balls were achieved simultaneously:

1.

reducing the particle size of magnetite, ash and eggshell;

2.

individual functionalization of each waste (eggshell, ash) with magnetite particles;

3.

a new, nanosized material in which the double functionalization of the eggshell with ash particles functionalized with magnetite was achieved simultaneously with the loading of the pores of the eggshell surface with the magnetite particles.

By modifying the structure of the two wastes from the composition of the newly obtained material (decreasing the number of pores) leads to increased surface areas, confirmed by the results of the BET analysis (Table 1) and implicit sorption sites suggesting an improvement of adsorbent properties.FT-IR studiesFigure 15 shows the IR spectra for EFM adsorbent raw materials (magnetite, fly ash and eggshell).Figure 15IR spectra for adsorbent raw material samples (magnetite, eggshell and fly ash).Full size imageFT-IR spectra for EFM engineered adsorbent are presented in Fig. 16.Figure 16FT-IR spectra of adsorbent (both molar ratios: M1 and M2) and its raw materials.Full size imageThe FT-IR spectra for adsorbent (at the both molar ration: M1 and M2) presents the vibrational bands characteristic of magnetite at 589 and at 432 cm−1associated with Fe–O stretch vibration46. The peaks assigned to the fly ash component: at 588 cm-1 Ca O group, at about 670 cm−1 attributed to the Al–O–Al bending vibration, at 1100 cm−1 is associated with X–O (X = Al, Si) and asymmetric stretching vibrations and band at 830 cm−1 specific to AlO4 coordination16,22,47,48,49. In addition, in the adsorbent FTIR spectra (Fig. 16) were found the characteristic IR bands eggshell component (Fig. 15). Thus, peak at 712 cm−1 (correspond to CaO stretching vibration), peaks at 875 and 1423 are attributed to C–O stretching vibration. The bands at 1798 and 2515 cm−1 are associated with O–C–O and peaks at 2875 respectively at 2981 cm−1 are due to CH– symmetric and asymmetric stretching vibration15,31,50. The position of O–H peak at 3740 cm−1 indicates the presence of moisture and water molecules15,22. As expected, the intensity of the peaks differs in M1 and M2, due to the different molar ratio between two of the raw materials that are part of the adsorbent component (fly ash and eggshell). These results are in close agreement with the literature and theoretical values confirms the presence of magnetite, fly ash and eggshell in adsorbent (at both molar ration: M1 and M2).Thermogravimetric analysisFigure 17a presents the thermal analysis results for fly ash sample.Figure 17(a) Thermogravimetric analysis of the fly ash sample in the range of 30–500 °C with a heating rate of 10 °C/min in open aluminum crucibles in the air atmosphere. (b) Thermogravimetric analysis of the eggshell with a heating rate of 10 °C/min up to 500 °C.Full size imageThe thermal analysis performed in the interval 30–500 °C highlighted two stages of decomposition. The first stage takes place in the range of 30–49 °C with a loss of 0.22% of the sample mass. This decomposition can be attributed to water loss. This process is visible in the DTG curve with a maximum at 45.5 °C, but also on the Heat Flow curve with a maximum at the same temperature and characterized by an exothermic process with ΔH = − 12.44 J/g. The second process presents a continuous thermal decomposition with a maximum observable on the DTG and HF curve at 480 °C, characterized by an exothermic effect. The decomposition does not end in the studied interval. The total weight loss is 2% of the sample mass16.The thermal analysis, in the range of 30–500 °C, performed for the eggshell sample (Fig. 17b), revealed a complex thermal decomposition. This decomposition has several stages that are difficult to separate. It is known that, in addition to inorganic calcium carbonate compounds, in the eggshell are present a multitude of organic components such as: proteins as main constituents, small amounts of carbohydrates and lipids51,52.At the same time, uronic acid is also present, which plays an important role in the resistance of the shell, such as sialic acid in very low concentration and two glycosaminoglycans, including hyaluronic acid, as well as a copolymer consisting of chondroitin sulfate-dermatan sulfate. There is also limited information on variations in nitrogen concentrations and the amino acid composition of the eggshell. A better understanding of the chemicals present in the composition of the eggshell is very important for its application in various fields, including for the purpose of absorbent material.The analysis of the TG curve highlights three hardly separable decomposition stages, the last of which is characterized by a complex multistage decomposition process. It is observed that in the interval 30–100 °C which can be attributed to dehydration, followed by the loss of crystallization water in the range 100–266 °C (4.8% of the sample mass) and then the complex decomposition of organic components in different stages depending on their stability until at 500 °C15.The last decomposition stage results in the loss of 80% of the total mass of the sample. Over 500 °C the decomposition of the inorganic component takes place, namely Ca carbonate. It can be seen that in the analyzed sample the weight of inorganic component is relatively small, namely 12.2% of the sample mass. During the decomposition stage from the interval 266–500 °C several maxima are observed on the DTG curve, which led us to conclude that simultaneous decompositions of several organic compounds take place, observing maxima at 345, 363, 374, 403, 408, 412, 430, 466 and 470 °C. The same main decomposition steps are faintly visible and the HF curve with processes in most cases exothermic. At temperatures higher than 266 °C and on this curve are visible several processes, most of which are exothermic, which can be attributed to the oxidation of organic compounds and their decomposition. The residue left after the thermogravimetric study (performed up to 500 °C) is calcium carbonate15,53.Subsequently, the mixture of the two wastes was analyzed in the two molar ratios:eggshell:fly ash = 3:1 and eggshell:fly ash = 1:3, respectively.The profile of thermogravimetric analysis for the binary mixture eggshell:ash fly in a 1:3 mass ratio performed in the range of 30–500 °C is depicted in Fig. 18.Figure 18Thermogravimetric analysis for eggshell: fly ash binary mixture in a 1: 3 mass ratios obtained in the range of 30–500 °C.Full size imageThe thermal analysis performed in the case of the binary mixture of eggshell and ash, in a 1:3 molar ratios, highlights the decomposition stages of the two components. Namely, the stage of water loss within the ash is visible, to which is added the loss of moisture observed in the case of the eggshell. On the HF flow is visible the exothermic process with a maximum of 45 °C and a ΔH = − 17.023 J/g which represents a sum of the two processes mentioned above. Two other processes are visible on the TG curve, one in the temperature range, 51–213 °C, with a mass loss of 0.27% of the sample mass. Then followed by a loss of 1.74% in the temperature range 213–405 °C. The thermal decomposition continues even above this temperature and the decomposition process was not completed in the studied temperature range.A thermogravimetric study was performed for the same binary mixture but in the eggshell:fly ash = 3:1 molar ratio. The results are presented in the next figure (Fig. 19).Figure 19Thermogravimetric analysis for eggshell: fly ash binary mixture in a 3:1 mass ratio obtained in the range of 30–500 °C.Full size imageIn the case of the thermal analysis of the binary mixture of eggshell and ash in a 3:1 molar ratio, the decomposition stages and the thermal behaviour of the individual components in correlation with the mixing ratio are very clearly visible.Magnetic measurementsThe magnetic properties of the samples: magnetite, M1 and M2 were investigated with an induction hysteresis-graph at low frequency driving field (50 Hz)54. And the hysteresis loops are presented in Figs. 20, 21 and 22. It was found that the samples reveal ferromagnetic behaviour and from the measured hysteresis loops the saturation magnetization ((sigma_{S})), the coercive field (Hc) and the remnant magnetization ((sigma_{R})) were determined. The results are presented in Table 3.Figure 20The hysteresis loop of sample M2.Full size imageFigure 21The hysteresis loop of sample M1.Full size imageFigure 22The hysteresis loop of magnetite.Full size imageTable 3 The values of coercive field (Hc) and remnant magnetization ((sigma_{R})) of M1, M2 and magnetite sample.Full size tableAs expected, the largest value of the saturation magnetization is that of the sample consisting entirely of magnetite. By diminishing the content of ashes from the thermal power station (from three parts in M2 sample to one part in sample M1) a small increase of the saturation magnetization was observed, from 14.06 to 15.12 emu/g (see Table 3). This can be explained by the presence of diamagnetic compounds within the ashes of the thermal station, the decrease of which led to the increase in the saturation magnetization of the sample M1, as compared to the sample M2. All three samples have small values of the remnant ratio, (sigma_{R} /sigma_{S}), which is an indication of the ease with which the magnetization reorients to the nearest easy axis magnetization direction after the remove of magnetic field.The dependencies on frequency of the complex magnetic permeability of the samples, (mu left( f right) = mu^{prime}left( f right) – imu^{primeprime}left( f right)), measured at room temperature, over the frequency range 3 kHz to 2 MHz are presented in Fig. 23. The measurements were performed using an Agilent LCR-meter (E-4980A type) in conjunction with a coil containing a vial in which the samples were placed. Details on the method of measurements of the real, (mu^{prime}left( f right)) and imaginary, (mu^{primeprime}left( f right)) components of the complex magnetic permeability are given in a previous study55.Figure 23Frequency dependence of the magnetite, M1, M2 of the complex magnetic permeability.Full size imageIn the frequency range in which the measurements were made, samples M1 and magnetite exhibits visible relaxation peaks of (mu^{primeprime}left( f right)), at the frequency of 30 kHz. Even if the M1 sample and the M2 sample have the same amount of magnetite, due to the diamagnetic compounds in the fly ash, the relaxation peak of the M1 sample is very attenuated (little visible, almost missing).Given the small size of the magnetite particles in the samples (on the order of tens of nanometers), they do not have a multi-domain magnetic structure. Thus, the only magnetic relaxation process, measurable in the radio frequency field, is the Neel relaxation process. The Néel relaxation time, (tau_{N}) is given by Eq. (15)56$$tau_{N} = tau_{0} exp left( {frac{Kv}{{k_{B} T}}} right)$$
(15)
where K is the effective anisotropy constant of the material from which the magnetic particles are made of, v is the magnetic volume of particles, kB is the Boltzmann’s constant, T is the temperature and (tau_{0}) is a constant in order of 10–9 s56.Assuming that the frequency dependence of the complex magnetic permeability,(mu left( f right) = mu^{prime}left( f right) – imu^{primeprime}left( f right)) obeys the Debye dispersion relations, then the frequency corresponding to the maximum of (mu^{primeprime}left( f right)) is correlated with the relaxation time by the relation, (2pi {kern 1pt} ftau = 1). For measurements at room temperature, with f = 30 kHz, the magneto-crystalline anisotropy constant of magnetite, K = 1.1 × 104 J m−3 and (tau = tau_{N}), under assumption of spherical shape of particles, one gets a magnetic diameter of the magnetite particles, d = 18.2 nm. This value compares favourably with the values measured by SEM and X-ray diffraction.Adsorption propertiesEffect of adsorbent dosageFigure 24a and b show the relationships between different material dosage and the cadmium removal efficiency and respectively adsorption capacity.Figure 24(a) The relationship between different material dosage and the cadmium removal efficiency. (b) The relationship between different material dosage and the cadmium adsorption capacity.Full size imageAccording to the Fig. 24a and b, cadmium removal efficiency and adsorption capacity depending on the amount of adsorbent used shows an upward trend (for quantities between 0.05 and 0.25 g), reaches a maximum of 0.25 g adsorbent (99.9% and 75.48 mg/g for M1 and respectively 99.8% and 75.46 mg/g for M2), after which both removal efficiency and heavy metal adsorption capacity gradually decrease with the increase in the adsorbent dose (0.3 g). These results suggest that the increased amount of adsorbent provides a supplement to the free active sites, but after reaching equilibrium, it leads to the formation of agglomerations and consequently to a decrease in the number of available active sites22,23.Effect of initial concentration on cadmium removal efficiencyFigure 25a shows the influence of heavy metal initial concentration on cadmium removal efficiency. It can be seen that removal efficiency shows an upward trend simultaneously with the increase of the initial cadmium concentration in the range 0–33.5 mg/L. The maximum removal efficiency (99,9% for M1 and respectively 99.8% for M2) was reached at a concentration of 28.5 mg/L, after which the decrease in cadmium removal efficiency begins.Figure 25(a) Relationship between initial concentration and removal efficiency (%). (b) Relationship between initial concentration and adsorption capacity (mg/g).Full size imageAccording to the Fig. 25b, in the same cadmium concentration range (0–33.5 mg/L), the adsorption capacity shows a similar trend, reaching a maximum at 28.5 mg/L (75.48 mg/g for M1 and respectively 74.46 mg/g for M2), and after which gradually decreases.These results indicate the initially an increase in the concentration of heavy metal causes an increase in the amount of Cd2 + ions and implicitly in the possibility of interaction with the active sites of the EFM adsorbent. And after reaching equilibrium, the amount of available metal ions is disproportionate compared to the decreasing number of free sites in the adsorbent, causing a decrease in the adsorption efficiency of the new engineered magnetic adsorbent used in the study15,57.Effect of pHThe wastewater pH is one of the top parameters with highly influence on the adsorption process efficiency having impact direct on the adsorption rate and adsorption capacity as fluctuations in the pH value of the solute induce changes in the degree of ionization of the adsorptive species and the of adsorbent surface23,57.In this study was investigated the pH influence toward the cadmium removal using the prepared material in the pH range of 3.0–7.0, to avoid the precipitation of Cd(OH)2 at pH values  > 715.According to the experimental results presented in Fig. 26a and b, the increase in pH value (between pH 3 and pH 6) leads to a significant increase in adsorption efficiency and adsorption capacity. The adsorption efficiency and adsorption capacity reach a maximum value (99.9% and 75.48 mg/g for M1 and respectively 99.8% and 75.46 mg/g for M2) at pH 6.5, after which it decreases slightly. This could be explained as follows: at low pH values is a competition between protons and Cd2+ to occupy the active sites of the adsorbent, even if they are available in large numbers. An increase in pH simultaneously leads to a decrease in the competition of protons and electrostatic repulsion forces, which induces an increase in cadmium removal efficiency. At pH  > 6.5 the removal efficiency begins to decrease as increased hydroxyl ion generation occurs to the detriment of Cd2+ ions. Therefore, the optimal pH 6.5 was chosen for subsequent experiments15,16,25,58,59.Figure 26(a) Effect of pH variation on cadmium removal efficiency. (b) Effect of pH variation on adsorption capacity.Full size imageEffect of contact timeFigure 27a showed the relationship diagram between the contact time and cadmium adsorption capacity.Figure 27(a) Effect pf contact time on cadmium adsorption capacity (mg/g). (b) Effect of contact time on cadmium removal efficiency (%).Full size imageIt can be observed from the Fig. 27a and b that the increase of the contact time determines an increase of the adsorption capacity and of the removal efficiency respectively. Both reached the maxima at 120 min The maximum of cadmium adsorption capacity was 75.48 mg/g for M1 respectively 75.46 mg/g for M2, and the maximum of removal efficiency was 99.9% for M1 and respectively 99.8% for M232.This performance can be attributed to the higher surface, the microporous structure that results from the experimental conditions of this study15.The analysis of this diagram indicates that the adsorption of cadmium takes place in three distinct phases:

1.

0–90 min, characterized by adsorption is fast due to the large number of active sites available on the surface of the adsorbent.

2.

the second phase, 90–120 min, the adsorption is slower which can be attributed to the diminution of the free adsorbent active sites;

3.

phase three:120–330 min, corresponds to the time interval in which there are no more free sites on the surface of the adsorbent and the adsorption has reached equilibrium.

According to the experimental results, optimum time in which the adsorption reaches an equilibrium is 120 min and was selected for the next investigations16,60.Effect of temperature on absorption processTemperature represents a key parameter in adsorption process. Therefore, the influence of temperature on cadmium adsorption on prepared material in the two different molar ratios (both M1 and M2) was investigated in the range of 5–50 °C (278.15–323.15 K). The cadmium removal efficiency and adsorption capacity increase first and then a very slight decrease occurs with the increase of temperature (Fig. 28a,b).Figure 28(a) Relationship between temperature and heavy metal removal efficiency. (b) Relationship between temperature and heavy metal adsorption capacity.Full size imageAt 25 °C the maximum removal efficiency is reached (99.89% for M1 and respectively 99.64% for M2). At the same temperature the heavy metal adsorption capacity is maximum of 75.48 mg/g for M1 and 75.43 mg/g for M2. This can be explained by the fact that within this temperature range indicated the favorability for the heavy metal mobilization and thus contact between cadmium and active sites from adsorbent. The relationship between temperature and cadmium adsorption effect indicates that in the range of 5–25 °C the cadmium absorption on prepared material is an endothermic process (physical adsorption). At 25–50 °C the adsorption process becomes exothermic and chemisorption occurs. However, the removal efficiency remains very high even at a temperature of 50 °C (98.78% for M1 and respectively 97.74% for M2).Comparison of cadmium removal efficiency for with other adsorbentsA comparison of cadmium removal efficiency of the newly engineered adsorbent (EFM) with other adsorbents reported in literature is presented in the next table (Table 4).Table 4 Comparison of the removal efficiency of newly nanosized magnetic adsorbent (at both molar ratios: M1 and M2) with the one reported in the literature (selected study) for some adsorbent materials that use the similar waste.Full size tableThe performance of the nanosized adsorbent EFM (at both molar ratios) can be attributed to the higher surface area, the microporous structure that results from the experimental conditions of this study15.Comparison of cadmium removal efficiency with the raw materialsThe removal efficiency of EFM adsorbent compared to that of its raw materials (fly ash, eggshell and magnetite) was investigated as the effect of contact time on the adsorption process. The relationship between the removal efficiency and contact time is presented in Fig. 29. It can be observed that there is an increase in the efficiency of removing heavy metal for all five investigated adsorbents (eggshell, ash, magnetite, M1 and M2) with a maximum of two hours of contact. According to the experimental results presented in Fig. 29, the best cadmium removal efficiency was obtained for M1 (99.89%) and 99.80% for M2, followed by eggshell (95.23%), fly ash (76.31%) and magnetite (71.44%).Figure 29Relationship between adsorbents removal efficiency and contact time.Full size imageThen, a very slight decrease occurs with the increase in contact time. These results confirm the cadmium removal efficiency dependence on the specific surface area and pores (number of available active sites) of the adsorbent used (Table 1).The maximum cadmium removal efficiencies determined experimentally in this study for the raw materials (eggshell, ash and magnetite) corroborated with the data reported in the literature15,25,33,61.Adsorption IsothermsThe absorption mechanism evaluation can be performed through an isotherm adsorbent study. The equilibrium isotherm plays a key role in the investigation of the adsorption behaviour.Due to their simplicity and convenient accuracy, Langmuir and Freundlich’s models are the most commonly used to adjust an adsorption process.Langmuir models provides information on the interaction between the solute and the monolayer surface of the adsorbent. The main working hypotheses of this model are: (1) adsorbent surface consists of uniform, identical sites distributed on the surface of adsorbent (2) adsorbent process takes place only on the surface of the adsorbent and (3) no contact between adsorbed molecules on the surface of the adsorbent.Freundlich model is appropriate to monolayer and multilayer adsorption processes on multiphase surfaces. This isotherm gives an expression on adsorbent surface heterogeneity and the variation in the heat of adsorption process. The applicability of the Freundlich model is limited by adsorption processes that take place at high pressures, but this restriction does not apply to the Langmuir model.These two adsorption isotherm models were applied in order to identify and implement an optimal model that adequately reproduces the experimental results obtained in this study were employed to study the mechanism of cadmium adsorption on the prepared material60,62.The parameters calculated as well the coefficient of correlation (R2) for both Langmuir and Freundlich models are presented in the Table 5.Table 5 Parameters of adsorption Langmuir and Freundlich isotherms for cadmium adsorption.Full size tableAs shown in the Table 5 both models fitted well for the experimental results. The maximum capacities calculated are close to the values for each component of the prepared adsorbent material (magnetite, eggshell and fly ash) and maximum capacities obtained at equilibrium (Table 5)22,23,57,63,64. However, according to the values of the correlation coefficient, R2, the behaviour of cadmium absorption suits better with Freundlich model (the higher correlation coefficient) suggests that the adsorption for cadmium ions was a multi-molecular layer adsorption process. The values for the Langmuir constant, KL, or equilibrium parameter for absorbent (the both molar ratios, M1 respectively M2) falls within the range 0  1 indicated a favourable adsorption process. Moreover, Freundlich dimensionless constant n values having greater than 1 suggests a favourable adsorption process that occurs on the investigated EFM adsorbent heterogeneous surfaces62,65,66.Adsorption kinetic studyThe kinetic models provide information on the efficiency of the adsorbent, the dynamic parameters (rate, time, etc.) of the adsorption process. The cadmium adsorption process on the prepared material was investigated employing linear and non-linear of pseudo-first-order (Eq. 6) pseudo-second-order (Eq. 7) and intraparticle diffusion models (Eq. 8) to fit the obtained experimental adsorption data. The Fig. 30a–c depicted the plots of the first-order, second-order and intraparticle diffusion models for the cadmium adsorption on nano-engineered adsorbent (EFM).Figure 30(a) Pseudo first-order model fitting diagram. (b) Pseudo second-order model fitting diagram. (c) Intraparticle diffusion model fitting diagram.Full size imageThe kinetic parameters were obtained from the slope and intercept of the fitting plots of adsorption reaction models: pseudo first-order model (the correlation between log(qe-qt) against time), respectively the pseudo second-order model (correlation between t/qt function on time) and adsorption diffusion model: intraparticle diffusion model (the plot as function of ({t}^{1/2})).The results of fitting parameters on these kinetic models are presented in Table 6.Table 6 Kinetic parameters for cadmium adsorption on nano-engineered adsorbent (EFM) at both molar rations (M1 and M2).Full size tableAccording to the data obtained in Table 6, the coefficients of adsorption reaction models have both values close to one, slightly differing only at the fourth decimal. It could suggest that cadmium removal is achieved through a physical and chemical adsorption process. It must be noted that were obtained higher values for the correlation coefficient (R2) and the calculated adsorption capacity value is very similar to those determined experimentally in the case of the pseudo-second-order kinetics model. Therefore, pseudo-second-order kinetic model was more suitable to describe the adsorption process. This indicating a chemical adsorption is assumed as the rate-limiting step for the cadmium adsorption on prepared material, involving an electron exchange between adsorbent and adsorbate (cadmium occurs with formation of strong chemical bonding)23.According to the Fig. 30c the allure of intraparticle diffusion model includes three regions. The first region corresponds to a boundary diffusion (cadmium diffusion on the prepared material exterior surface). The second region is related to heavy metal intraparticle diffusion into the pores of nano-engineered adsorbent (EFM). The third region represent the cadmium adoption into the interior site of the EFM. Since, the slope of the three regions gradually decreases (Ki1  > Ki2  > Ki3) is assumed that boundary diffusion is the limiting region, followed by intraparticle diffusion15,67. The results indicate that beginning of the adsorption process cadmium ions can be quickly bound on the prepared material exterior surface. In the intraparticle diffusion process (second region) there is a gradual decrease in adsorption at the sites on the adsorbent surface (adsorption capacity reaches the maximum value). Then, cadmium adsorption takes place on the available sites inside the adsorbent, generating significant mass transfer resistance and reaching the adsorption equilibrium and the adsorption rate gradually decreases68,69,70.The adsorption models used provide information on both the performance of the prepared material and a perspective of the adsorption mechanism.Thermodynamical studyThe Gibbs free energy in adsorption process was calculated according to the corresponding equation (Eq. 10). The thermodynamic parameters ΔS and ΔH were obtained from the slope and intercept of the adsorption thermodynamic curve. The obtained results are presented in next table (Table 7).Table 7 Thermodynamic parameters for the cadmium adsorption on adsorbent.Full size tableFrom these data obtained (Table 7) can be found that the free energy variation value of the adsorption process has negative values (ΔG  α lower than 0.05 (α = 0.05), which suggests that between the M1 and M2 there are not statistically significant differences. More

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The data reported in this study expands upon the present knowledge concerning the mineralogy of coralline algae species worldwide, encompassing for the first time coralline algae species data from the Southwest Atlantic Ocean, where this group is the main frame-builders in coral reefs and the major inner component in rhodoliths16,26.Mineralogical analysis revealed that coralline algae species of the Brazilian Shelf were mainly formed of high-Mg calcite. Six coralline algae species in this study had the same range of high-Mg calcite, between 80 and 100%, than the same species from different regions of the world: Lithophyllum corallinae, Lithophyllum kaiseri (as Lithophyllum congestum), Lithophyllum stictaeforme, Lithothamnion crispatum, Melyvonnea erubecens (as Lithothamnion erubecens) and Sporolithon episporum (Table S2). This result confirms that species from different families, such as Corallinaceae, Hapalidiaceae and Sporolithaceae have a CaCO3 skeleton formed mainly of high-Mg calcite.In agreement with earlier studies, the average high-Mg calcite content in Corallinaceae was very similar to the results compiled by Smith et al.11 (96.7 wt.% and 96.2 wt.%, respectively). This pattern was also observed for Hapalidiaceae, which presented a mean value of 88.9 ± 3.6 wt.% in our study and 90.2 wt.%. However, Smith et al.11 registered a high-Mg calcite content of 98 wt.% for Sporolithaceae, while in our study this polymorph had a mean occurrence of 86.2 ± 6.5 wt.%. This percentage can be attributed mainly to the lower content of high-Mg calcite found in Sporolithon yoneshigueae, which is an endemic species of the Brazilian Shelf27.The high similarity between the mineralogy (% high-Mg calcite, % aragonite and % dolomite) of the species belonging to three encrusting algae families, revealed by the cluster analysis, emphasizes the lack of CaCO3 disparities over skeleton mineralogy of coralline algae at family level. This aspect was also evidenced by several studies concerning coralline algae mineralogy11,21,22,23,24,25. This fact was confirmed in the cluster analysis between the mineralogy of the studied coralline species, in which samples from different families were grouped. Considering these findings, the mineralogical pattern exhibited by the crustose algae may not be driven by taxonomic classification, as was first proposed by Chave28. Therefore, the skeletal mineralogy from Brazilian coralline algae species can not be used as a taxonomic character, not even for higher taxonomic levels.In this sense, the mineralogical analysis from L. crispatum, the most common rhodolith-forming species on the Brazilian Shelf16, revealed that samples from the Abrolhos Bank presented higher high-Mg calcite in their composition, and the highest % of Mg substitution in the calcite lattice than the species from the other four regions studied. One of the possible explanations is that the Abrolhos Bank has the highest seawater temperature compared to the other four sites, which influences CCA mineralogy. This result corroborates the hypothesis that coralline algae species do not have a strict control over Mg precipitation as stated by Stanley et al.29. In addition to seawater temperature, Mg/Ca ratio in seawater can also affect the incorporation of magnesium into coralline algae skeletons11,29.In relation to other CaCO3 polymorphs, previous studies have registered some species with up to 20% aragonite11,12. Meanwhile, in this study, S. yoneshigueae presented CaCO3 skeletons formed of more than 30% of aragonite, which expands the range found in coralline algae for this polymorph. The high percentage of aragonite found in S. yoneshigueae could be related to the fact that this species presents larger overgrown calcified empty tetrasporangial compartments, in comparison with other Sporolithaceae species27, which could be filled with aragonite. This feature has mainly been described in the overgrown conceptacles of Lithothamnion sp.30 and in cell infills of Porolithon onkodes31. The presence of aragonite could be also attributed to the use of aragonite granules in the sediment to repair any damage in the alga-substrate attachment32.Raman mapping showed the presence of high-Mg calcite in the bulk of the cell wall with little aragonite in its inner part, which seems to form an inner “shell”, closer to the cell membrane. To date, this is the first study that has utilized Raman maps to show the localization of aragonite in cell walls of coralline algae. The maps consisted of the cellular living layer from the coralline algae crust, right beneath the epithelial cells, which indicates that the mineralization of aragonite occurred in live cells and it was probably not a remineralization process.Aragonite inside cell bodies was first seen by Nash et al.12 using Backscattered Scanning Electron Microscopy. They also reported the presence of dolomite or protodolomite, which were not observed herein by Raman spectroscopy, probably because of the low amount of this polymorph.Previous studies considered that the inclusion of dolomite into carbonate skeletons is a microbial-mediated process after cell death upon the discovery of microbial-associated dolomite formation in anoxic marine33 and freshwater environments34. The presence of several calcium carbonate polymorphs found in coralline algae raises the question of whether all these polymorphs are in fact synthesized by the algae.Indeed, the role of coralline algae in the different forms of calcium carbonate crystal precipitation is a crucial issue that should be addressed. Nowadays, studies calculate the production of CaCO3 by coralline algae based on CCA coverage35, without considering that not all CaCO3 produced in that structure is related to coralline algae biomineralization processes (e.g. secondary calcification processes such as infilling of the older skeleton and skeletal dissolution vs newly deposited carbonate). Therefore, it would be misleading to presume the net CaCO3 accretion of coralline algae structures without knowing the origin of the CaCO3 processes. This is also valid in relation to studies on the influences of atmospheric [CO2] rise on coralline algae, based on weight changes36,37,38 and its impacts on the mineralogy of the existing crust21.Concerning Mg2+ substitution in the high-Mg calcite lattice, we found that Brazilian encrusting algae possess a higher Mg-substitution (46.3% more Mg2+ than the global average) in calcite than specimens collected worldwide. A possible explanation for the higher mean Mg2+ content might be related to the high seawater temperatures39, as this was also observed along the tropical Brazilian Continental Shelf. This can be exemplified by the high Mg2+ content found in fourteen species that occur in warmer waters of the Brazilian Shelf, where the mean surface seawater temperature (SST) ranged between 26.4 and 29.8 °C (from 2008 to 2016), between 17°S and 3°N. The lower Mg2+ amounts presented in L. margaritae and L. attlanticum could also be explained by the temperature, as these species were collected at the southernmost site (27°S) in the temperate zone, where the mean SST (from 2008 to 2016) varied between 22.5 and 25 °C (NOAA Comprehensive Large Array-Data Stewardship System-CLASS: SST50). A relationship between the Mg2+ content and temperature has already been proposed in previous works39 and is widely accepted. Nash and Adey40, when plotting the data collected using XRD, found a very strong correlation coefficient (R2 = 0.975) between mol% MgCO3 in coralline algae and temperature. Moreover, the Mg/Ca rate in coralline algae is used as a proxy archive41 and to generate multicentury-scale climate records from extratropical oceans42.Although seawater temperature is loosely associated with latitude, the New Zealand species, for example, are subjected to lower temperatures (2012 annual maximum and minimum surface seawater temperatures: 21 and 18.7 °C, respectively), while Caribbean and Cocos Island algae grow at higher temperatures (2008–2016 annual maximum and minimum surface seawater temperatures: 29.5 and 23.4 °C, respectively) (NOAA Comprehensive Large Array-Data Stewardship System – CLASS: SST50). If we consider the differences in temperature (≅ 6 °C) and Mg2+ content difference (7.67 wt.%) between the sampling sites along the Brazilian Shelf, we can infer that there is an average increase of 1.27 wt.% of Mg2+ per °C. This value is in the range from 0.4 to 2 wt.% Mg per °C reported previously, both in experimentally and in situ studies39.This relationship between Mg substitution and temperature is also critical in face of the temperature risen episodes that we are seeing all over the world43, including the Brazilian Shelf44. If coralline algae produces High Mg calcite with more Mg substitution in higher seawater temperatures, these thermal anomalies could force the production of a highly soluble polymorph, making coralline algae skeleton even more prone to dissolution.It is well known that high-Mg calcite is the most soluble CaCO3 crystalline polymorph under acidified conditions and that this dissolution is more evident when Mg substitution in the calcite lattice is higher45. In our study 70% of the coralline algae species presented a Mg substitution in the range of 12 to 24% and the mean Mg substitution was 21.1%, which reinforces the susceptibility of Southwestern Atlantic coralline algae to future high [CO2] scenarios.Even though previous experiments using synthetic calcium carbonate showed that the rise of seawater temperature increases Mg substitution, making high-Mg calcite more stable46 and other studies claiming that coralline algae with higher Mg substitution (more than 24% in average) presented less dissolution when exposed to high [CO2]13, Southwestern Ocean coralline algae are already living in a limit situation, where seawater can reach temperatures up to 28 ºC. Since we have a correlation between Mg substitution and temperature around 1.27% Mg per 1 ºC, it would take 2.4 to 6.2 ºC rise so the alga starts to produce a more stable calcite polymorph. Such a temperature rise could be lethal to these algae, also promoting a surface microbial shift that could be crucial to sucectional processes (e.g. settlement) involving other marine organisms, such as corals, which is critical for reef regeneration and recovery from climate-related mortality events47. The comparisons of results obtained through assays with synthetic calcium carbonate must be done with caution, because it should be take into account that the complex calcium carbonate biomineralization processes performed by marine organisms are highly dependent of a narrow range of environmental conditions.In face of the dependency of these environmental conditions, the broad range of Mg content in temperate coralline algae25, a high inter species variability in the % Mg in this study (Abrolhos Bank; 14.5 to 28.8% Mg), as well as an anatomical difference in Mg content in coralline algae40, suggest that other environmental parameters (e.g. Mg/Ca in seawater, light, salinity, etc.) could also drive Mg substitution in coralline algae. Furthemore, coralline algae biological processes might exert some kind of control over Mg-calcite calcification which make them more resilient under rising CO239.Long-term projections of ocean acidification and the CaCO3 saturation state indicated that high-latitude seawater will be undersaturated with respect to high-Mg calcite in the second half of this century45. Early results with coralline algae Sonderophycus capensis and Lithothamnion crispatum in a subtropical mesocosm in Brazil showed that an increase in seawater pCO2 (1000 ppm) enabled both species to continue photosynthesizing but did cause carbonate dissolution48.However, coralline algae from the North Atlantic Ocean, where the temperatures are lower, presented the lowest Mg substitution mean (11.91%), with some algae presenting only 8% of Mg substitution. This fact confers a more stable calcite skeleton to face ocean acidification then individuals from tropical environments. In addition, coralline algae from the Southwestern Atlantic Ocean are already living at temperatures that can be considered a limit for their survival. In fact, for cold water species, a subtle temperature increase could be beneficial in terms of their metabolism, photosynthesis and biomineralization.By the year 2100, surface seawater in all climatic zones could be undersaturated or at metastable equilibrium, with a high-Mg calcite phase containing ≥ 12 mol% Mg45. This could be catastrophic to coralline algae from the Southwest Atlantic Ocean, which produce CaCO3 crystals with more than 20% of Mg substitution in average as shown by the present study and for all the carbonate structures (e.g. rhodolith beds, coralline reefs, etc.) that depends on these skeletons to maintain and grow.It is worth to mention that coralline algae are present since the Mesozoic, in particular Sporolithaceans, which were already abundant in Cretaceous shallow waters49 and have already been submitted to bigger climate change events in the past, such as the Paleocene-Eocene Thermal Maximum (PETM), in which the deep-water temperature increased ∼5 ºC and a massive carbon cycle change took place with a large amount of CO2 absorbed by the oceans50. One of the possible explanations for the survival of coralline algae is that their biomineralogical control is limited to polymorph specification and would be ineffectual in the regulation of skeletal Mg incorporation51. In this sense, in past geological eras, such as the Cretaceous and Paleogene, the Mg/Ca ratio of the oceans favors the precitation of low Mg calcite29,52, which are more stable to dissolution. In a parallel to present day, other fundamental aspect we should take into account is the speed of progression of these changes. Actually, we know that the fast evolution of temperature and acidification present scenarios may result in significant impact on marine biodiversity and in marine calcium carbonate cycle players, as reef organisms and CCA.Carvalho et al.53 proposed that there would be a suitable area for rhodolith occurrence around 230,000 km2, providing a new magnitude to Brazilian Continental Shelf relevance as a major world biofactory of carbonate. In fact, this work confirms the estimation from previous studies, which indicated that this area would correspond to a 2 × 1011 tons of carbonate deposit of the Brazilian coast53. Among the most critical regions in the Brazilian coast, the Abrolhos Bank encompasses the largest continuous latitudinal rhodolith beds registered to date6, which is responsible for the production of approximately 0.025 Gt−1 year−1 of calcium carbonate, similar to those values reported for major tropical reef environments54,55. Another recently described important reef area on the Brazilian Shelf is an extensive carbonate system (≅ 9500 km2) off the Amazon River mouth56, which is composed of mesophotic carbonate reefs and rhodolith beds. These huge carbonate reservoirs and biodiversity hotspots may undergo a major decline if global ocean acidification and temperature rise take place in the near future. More

Assembly and annotation of the chloroplast genomesAssembly resulted in a whole cp genome sequence of C. hirtinoda with a length of 139, 561 bp (Fig. 1), consisting of 83, 166 bp large single-copy region, 20, 811 bp small single-copy regions, and two 21,792 bp IR regions, comprising the typical quadripartite structure of terrestrial plants. The cp genome of C. hirtinoda was annotated with 130 genes, including 85 protein-coding genes, 37 tRNA genes, and 8 rRNA genes (Table 1). Most of the 15 genes in the C. hirtinoda cp genome contain introns. Of these, 13 genes contain one intron (atpF, ndhA, ndhB, petB, petD, rpl2, rpl16, rps16, trnA-UGC, trnI-GAU, trnK-UUU, trnL-UAA, trnV-UAC) and only the gene cyf3 includes two introns, and the gene clpP intron was deleted (Supplementary Table S1). The rps12 gene contained two copies, and the three exons were spliced into a trans-splicing gene18.Figure 1Chloroplast genome map of C. hirtinoda. Different colors represent different functional genes groups. Genes outside the circle indicate counterclockwise transcription, and genes inside the clockwise transcription. The thick black line on the outer circle represents the two IR regions. The GC content is the dark gray area within the ring.Full size imageTable 1 Summary of the chloroplast genome of C. hirtinoda.Full size tableThe accD, ycf1, and ycf2 genes were missing in the cp genome of C. hirtinoda, and the introns in the genes clpP and rpoC1 were lost. This phenomenon is consistent with previous systematic evolutionary studies on the genome structure of plants in the Poaceae family19. The phenomenon of missing genes is reported in other plants20,21,22,23.The total GC content in the C. hirtinoda cp genome was 38.90%, and the content for each of the four bases, A, T, G, and C, was 30.63%, 30.46%, 19.57%, and 19.33%, respectively (Table 2). The LSC region (36.98%) and SSC region (33.21%) exhibited much lower values than the IR region (44.23%), indicating a non-uniform distribution of the base contents in the cp genome, probably because of four rRNAs in the IR region, which in turn makes the GC content higher in the IR region. These values were similar to cp genome results previously reported for some Poaceae plants24,25.Table 2 Base composition in the C. hirtinoda choloroplast genome.Full size tableRepeat sequences and codon analysisSSR consists of 10-bp-long base repeats and is widely used for exploring phylogenetic evolution and genetic diversity analysis26,27,28,29.In total, 48 SSRs were detected in C. hirtinoda, including 27 mononucleotide versions, accounting for 56.25% of the total SSRs, primarily consisting of A or T. Additionally, four dinucleotide repeats consisting of AT/TA and TC/CT repeats, and 3 tri, 13 tetra, and 1penta-repeats (Fig. 2A). From the SSRs distribution perspective, the majority (79%) of SSRs (38) were observed in the LSC area, whereas 6 SSRs in the IR region (13%) and 4 SSRs in the SSC region (8%) were discovered (Fig. 2B). Previous research suggests that the distribution of SSRs numbers in each region and the differences among locations in GC content are related to the expansion or contraction of the IR boundary30.Figure 2Analysis of simple sequence repeats in C. hirtinoda cp genome. (A) The percentage distribution of 45 SSRs in LSC, SSC, and IR regions. (B).Full size imageThe REPuter program revealed that the cp genome of C. hirtinoda was identified with 61 repeats, consisting of 15 palindromic, 19 forward and no reverse and complement repeats (Fig. 3). We noticed that repeat analyses of three Chimonobambusa genus species exhibited 61–65 repeats, with only one reverse in C. hejiangensis. Most of the repeat lengths were between 30 and 100 bp, and the repeat sequences were located in either IR or LSC region31 (Supplementary Table S2).Figure 3Information of chloroplast genome repeats of Chimonobambusa genus species.Full size imageWe identified 20,180 codons in the coding region of C. hirtinoda (Fig. 4, Supplementary Table S3). The codon AUU of Ile was the most used, and the TER of UAG was the least used codon (817 and 19), excluding the termination codons. Leu was the most encoded amino acid (2,170), and TER was the lowest (85). The Relative Synonymous Codon Usage (RSCU) value greater than 1.0 means a codon is used more frequently32. The RSCU values for 31 codons exceeded 1 in the C. hirtinoda cp genome, and of these, the third most frequent codon was A/U with 29 (93.55%), and the frequency of start codons AUG and UGG used demonstrated no bias (RSCU = 1).Figure 4Amino acid frequencies in C. hirtinoda cp genome protein coding sequences. The column diagrams indicate the number of amino acid codes, and the broken line indicates the proportion of amino acid codes.Full size imageComparative analysis of genome structureThe nucleotide variability (Pi) values of the three cp genomes discovered in the Chimonobambusa genus species ranged from 0 to 0.021 with an average value of 0.000544, as demonstrated from DnaSP 5.10 software analysis. Five peaks were observed in the two single-copy regions, and the highest peak was present in the trnT-trnE-trnY region of the LSC region (Fig. 5). The Pi value for LSC and SSC is significantly higher than that of the IR region. In the IR region, highly different sequences were not observed, a highly conserved region. The sequences of these highly variable regions are reported in other plants during examinations for species identification, phylogenetic analysis, and population genetics research33,34,35.Figure 5Sliding window analysis of Chimonobambusa genus complete chloroplast genome sequences. X-axis: position of the midpoint of a window, Y-axis: nucleotide diversity of each window.Full size imageThe structural information for the complete cp genomes among three Chimonobambusa genus species revealed that the sequences in most regions were conserved (Fig. 6). The LSC and SSC regions exhibit a remarkable degree of variation, higher than the IR region, and the non-coding region demonstrates higher variability than the coding region. In the non-coding areas, 7–9 k, 28–30 k, 36 k and other gene loci differed significantly. Genes rpoC2, rps19, ndhJ and other regions differ in the protein-coding region. However, the agreement between the tRNA and rRNA regions is 100%. A similar phenomenon has also been reported by others36.Figure 6Visualization of genome alignment of three species chloroplast genome sequences using Chimonobambusa hejiangensis as reference. The vertical scale shows the percent of identity, ranging from 50 to 100%. The horizontal axis shows the coordinates within the cp genome. Those are some colors represents protein coding, intron, mRNA and conserved non-coding sequence, respectively.Full size imageIR contraction and expansion in the chloroplast genomeDue to the unique circular structure of the cp genome, there are four junctions between the LSC/IRB/SSC/IRA regions. During species evolution, the stability of the two IR regions sequences was ensured by the IR region of the chloroplast genome expanding and contracting to some degree, and this adjustment is the primary reason for chloroplast genome length variation37,38.The variations at IR/SC boundary regions in the three Chimonobambusa genus chloroplast genomes were highly similar in the organization, gene content, and gene order. The size of IR ranges from 21,797 bp (C. tumidissinoda) to 21,835 bp (C. hejiangensis). The ndhH gene spans the SSC/IRa boundary, and this gene extended 181–224 bp into the IRa region for all three Chimonobambusa genus. The gene rps19 was extended from the IRb to the LSC region with a 31–35 bp gap. The rpl12 gene was located in the LSC region of all genomes, varied from 35–36 bp apart from the LSC/IRb (Fig. 7).Figure 7Comparison of LSC, SSC and IR boundaries of chloroplast genomes among the three Chimonobambusa species. The LSC, SSC and IRs regions are represented with different colors. JLB, JSB, JSA and JLA represent the connecting sites between the corresponding regions of the genome, respectively. Genes are showed by boxes.Full size imageThree chloroplast genomes of the Chimonobambusa genus were compared using the Mauve alignment. The results showed that all sequences show perfect synteny conservation with no inversion or rearrangements (Fig. 8).Figure 8The chloroplast genomes of three Chimonobambusa species rearranged by the software MAUVE. Locally collinear blocks (LCBs) are represented by the same color blocks connected by lines. The vertical line indicates the degree of conservatism among position. The small red bar represents rRNA.Full size imagePhylogenetic analysisWe performed a phylogenetic analysis using the complete chloroplast genomes and matK gene reflecting the phylogenetic position of C. hirtinoda. The maximum likelihood (ML) analysis based on the complete chloroplast genomes indicated seven nodes with entirely branch support (100% bootstrap value). However, the three Chimonobambusa genera exhibited a moderate relationship due to fewer samples used, supporting that C. hirtinoda is closely related to C. tumidissinoda with a 62% bootstrap value more than C. hejiangensis. A phylogenetic tree based on the matK gene revealed that Chimonobambusa species clustered in one branch was consistent with the phylogenetic tree constructed by the complete cp genome tree (Fig. 9). The results show that the whole chloroplast genome identified related species better than the former, consistent with the previous study39.Figure 9Maximum likelihood phylogenetic tree based on the complete chloroplast genomes (A) and matK gene (B).Full size image More

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