Revalorization of tidal waste as a sustainable cellulose source

doi:10.21203/rs.3.rs-5309720/v1

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Revalorization of tidal waste as a sustainable cellulose source

https://doi.org/10.21203/rs.3.rs-5309720/v1

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The increase of non-native species in the seas and eutrophication cause accumulation of seaweed and marine plants on coasts that become waste. Marine biomass residues are abandoned or sent to landfills at considerable cost. The aim of this work is to revalue tidal waste as a cellulosic material. Cellulose was isolated using minimal environmental impact (clean) technologies in two stages, first one with sodium hydroxide and anthraquinone, and the second with alkaline hydrogen peroxide. Product were characterized by evaluating the contents on α-cellulose, ash, ethanol-benzene extractives, holocellulose, hot water-soluble compounds and lignin, also, yield was calculated. For identifying the optimal operating conditions, we used response surface methodology (central composite design) which allowed to model the treatment conditions. Under optimal conditions, α-cellulose and holocellulose contents were 87.21% and 54.31% respectively. The hot water-soluble fraction of 30.81% can potentially be refined to obtain value-added compounds, allowing extraction to be adapted to an integrated biorefinery process.

Cellulose

Clean technologies

Optimization

Response surface methodology

Tidal waste

The increase of non-native species as well as eutrophication issues are promoting tidal waste and algal blooms that get drifted to the coastline due to winds, currents and tides generating thus marine biomass coastline depositions. These accumulations also known as beach wrack or beach cast, decompose and negatively affect the coastal ecosystem and local beach tourism (Hyndes et al. 2022). Depending on the coastal management of the region where the wrack accumulates, it can leave abandoned or send to landfills generating considerable costs (Robbe et al. 2021). The inappropriate management of these marine biomass cause the loss of a valuable resource that could be integrated in a circular economical process by creating new value-added products and energy (Macreadie et al. 2017).

Cellulose is the most abundant biopolymer on earth and presents versatile characteristics that make it a high valuable product (Du et al. 2018). In 2021, cellulose was the world's 466th most traded product and between 2020 and 2021 the exports of cellulose grew by 17% (Datawheel 2023). The European Union directive have approved legislation that already forbid the sale of single-use products made of plastics which will surely stimulate the production and consumption of biodegradable cellulose derived products in Europe as potential substitute, especially in packaging manufacture (European Parliament 2018). The sustainability of cellulose production will be a requirement for future supply. Indeed, wood raw materials will be not an enough procurement source for this high expected demand. Environmental problems associated with the increased use of wood sources has forced researchers to explore new options including non-wood and residual material sources that are all based on cellulose of terrestrial plant origin (Aguado et al. 2018; Moral et al. 2016).

Cellulose from marine and terrestrial species has similar structure and properties (Percival and Ross 1949), in contrast, lignin is mostly associated to terrestrial plants, being found in considerably lower proportions in algae or marine plants which is an advantage over the use of terrestrial plants since lignin has a recalcitrant nature and can cause technical difficulties in industrial activities (Baghel et al. 2021). Cellulose from marine sources has not been explored in depth existing a real lack of studies on its valorization. Recently, the incorporation of Posidonia oceanica into composites has been investigated (Abdel-Hakim and Mouradb 2023). Its chemical characterization shows that this marine plant present contents of 40% (w/w) and 60% (w/w) of α-cellulose and holocellulose respectively (Petrounias et al. 2023).

Enhancement of the efficiency of the cellulose extraction process is another turning point for increasing the production of cellulose. Soda-anthraquinone (NaOH-AQ) and alkaline hydrogen peroxide (AHP) treatments have demonstrated to be effective over agriculture residues for cellulose extraction (Cabrera et al. 2014). The alteration of the lignin structure occurs due to the saponification of the intermolecular ester bonds (Singh et al. 2015) which disperses the lignin and hemicellulosic fibres and expose the cellulose contained in the matrix. Anthraquinone (AQ) added as a catalyst in the sodium hydroxide (NaOH) stage can enhance lignin removal and limit the depolymerisation of cellulose (Liu et al. 2014). When the AHP is combined with a first NaOH-AQ treatment, the simple lignin like compounds can be solubilized and the AHP will only need to oxidize the recalcitrant ones (Hart and Rudie 2014; Liu et al. 2018). H₂O₂ can act as a delignification agent by eliminating conjugated carbonyl groups (p- or o-quinones, coniferyl aldehydes) from the lignin structure to produce carboxylic acids and aldehydes through oxidation by perhydroxyl anion (HOO⁻), whose concentration increases with temperature and an optimal pH of 11.50 (Bajpai 2012) d concentration and temperature at the time it can be considered as a clean process with water as degradation product (Campos-Martin et al. 2006). This work aims to evaluate the potential of beach wrack to obtain cellulose by studying the extraction in a combined NaOH-AQ and AHP treatment. By response surface methodology we investigated the influence of three factors, H₂O₂ concentration, temperature, and time, at three levels over chemical characterization parameters (α-cellulose, ash, ethanol-benzene extractives, holocellulose, hot water-soluble compounds and lignin)

Tidal waste collection and species identification

Beach wrack was collected raw and fresh after a bloom episode at “Playa de Las Canteras” (Las Palmas de Gran Canaria, Spain) in October 2022 according to the Spanish Catalogue of Threatened Species (Ministry of Environment, Rural and Marine 2011) and to the state regulations of article 57 of the Law on Natural Heritage and Biodiversity (Head of State 2007). At the laboratory, the beach cast was washed with high pressure water for better removal of mainly impurities and dried at room temperature. Macroscopical visualization took place for identifying the main species. Bibliography research of the morphological features of the species and their presence in the sampling beach were carried out.

Cellulose extraction

Alkaline treatment was carried out cooking 25 g of oven-dried marine biomass in a 600 ml compact mini bench top reactor (Parr Instrument Company 5500, U.S.A) using 4% NaOH w/w and 0.50% w/w AQ (to increase the selectivity towards lignin). The conditions of this step were liquid-to-solid (L/S) ratio 30 (ml/g), temperature 130 ºC and time 120 min. The reactor was connected to a control unit which included a motor actuating over the reactor stirring (by turnover). After this stage, the marine biomass was collected through filtration and washed with excess deionized water. AHP was carried out by adding H₂O₂ following a factorial experimental design. In each essay, the obtained product was mixed with deionized water, MgSO₄ and DTPA, these last two were added at fixed doses of 0.20% and 0.50% w/w respectively. pH was adjusted to 11.50 and L/S was held at 30. The sample was placed in a sealed polyethylene bag and kept at the desired temperature by means of a water bath. The product was quenched to room temperature, filtered, washed, air-dried, and stored.

Chemical characterization of cellulose pulps

TAPPI methods certified by the American National Standards Institute (ANSI) were used for the chemical characterization of the final product, except for holocellulose (HOL) that was determined according to the method of Wise et al. (Hubbel and Ragauskas 2010). The α-cellulose proportion (CEL) was determined following the TAPPI T203 cm-22 (TAPPI 1999), ash content (ASH) following the TAPPI T211 om-16 (TAPPI 2016), ethanol-benzene extractives content (EBE) according to TAPPI T204 cm-17 (TAPPI 2017), hot water-soluble compounds (HWS) with TAPPI T207 cm-22 (TAPPI 2022) and lignin content (LIG) with TAPPI T222 om-15 (TAPPI 2015). Yields (YI) were calculated by gravimetry.

Modelling and optimization (dup: abstract ?)

Temperature was set at 50, 60 and 70 ºC, time at 30, 60 and 90 min and the H₂O₂ concentration was 1, 3.50 and 6% w/w. A central facial composite design was used (2³+star points) which generated 15 different runs that were replicated two times. The inputs were normalized between − 1 and 1 (Table 1). The dependent responses were analysed by multiple regression through the least square method.

Table 1

Independent variables and their coded values.
Independent variable	Code	-1	0	+ 1
Temperature (ºC)	X_T	50	60	70
Time (min)	X_t	30	60	90
H₂O₂ (%)	X_P	1	3.50	6

We fitted the results to a second grade-polynomial model as shown in Eq. (1) to evaluate the influence of the input variables over the output ones.

$$\:Y=\:{\beta\:}_{0}+{\sum\:}_{i=1}^{k}{\beta\:}_{i}{x}_{i}+\:{\sum\:}_{i=1}^{k}{\beta\:}_{ii}{{x}^{2}}_{i}+\sum\:_{i=1}^{k}{\sum\:}_{\:j=i+1}^{n}{\beta\:}_{ij}{x}_{i}{x}_{j}\:\:\:\text{i}\le\:\text{j}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)\:$$

Y is the predicted response, k the number of independent variables, x_i and x_j the coded independent variables, β₀ the constant coefficient, βi, β_ii and β_ij the coefficients of linear, quadratic and interaction term, respectively (Montgomery 2018). For the estimation of models, 3D plots and optimization of the responses were performed (Dixon 1988). The significance of effects and interactions were estimated by ANOVA.

Species identification

Despite the heterogeneous nature of the tidal residue, four predominant species were identified (Fig. 1). Cymodocea nodosa is a seagrass composed by leaf bundles, which contain 7 to 9 veins and 1 mm diameter rhizomes. It forms sea meadows in southern locations of the Canary Islands, in Confital Bay where the collection beach is located (Pavón-Salas et al. 2000). The small brown yellowish thallus of the brown algae Lobophora variegata (Phaeophyta) allows to identify an erect habit with simple and lobed to lacerate blades. The height ranged from 1.50 to 5 cm, width from 2–6 cm and thickness from 100–130 cm. This alga conforms beach casts phenomenon in Canary Islands (Sangil et al. 2018).

Other abundant specie found was a parenchymatous algae with ribboned and flat axes. The brownish to olive-green thallus was branched dichotomous forming approximating angles of 15° to 35º and was isotomous (equal branches) like anisotomous (unequal branches). The wide of the branches range from 3 to 6 mm and apices were rounded and bilobed. The height of the thallus was up to 15 cm and the thickness ranged around 85–100 µm. These morphological features correspond to the algae Dictyota dichotoma (Phaeophyta) (Bogaert et al. 2020). This specie is widely spread in the North-eastern Atlantic marine ecosystems (Sangil et al. 2018). Another identified specie found to be a red alga (Rhodophyta) with thallus forming large clumps up to 20 cm high constituted of stoloniferous prostrate axes from which arise erect axes up to 2 mm wide. These axes were covered by dense radially arranged branches. The morphology suggests the specie Asparagopsis taxiformis, as it match with the bibliography (Zanolla et al. 2018). This specie was found in a lower amount compared to the others. It is known to form macroalgal communities in Macronesian Islands (Zárate et al. 2020). The marketability of this mixed biomass depends mostly on the periodicity of the depositions (Milledge et al. 2016). In the case of cellulose, as this natural polymer is present in almost all species of macroalgae and marine plants, the only limitation for the extraction is the amount present in each species.

A multistage process for cellulose extraction was carried on. The first NaOH-AQ stage was performed to increases the accessibility towards cellulose, dissolving the lignocellulosic matrix (Liu et al. 2018). In the AHP stage, the removal of lignin and impurities occurs using H₂O₂; the initial pH, temperature, reaction time and consistency are the most influential factors (García and Vidal 1984). The required pH for optimal operability ranges from 10 to 12 and the optimum temperature for lignin removal ranges from 60 to 80 ºC (Wuorimaa et al. 2006). The use of additives and stabilizers in the AHP process allow to stabilize chromophore groups by removing metals which can act as catalysators. They can also avoid the premature dismutation of peroxide. Magnesium salts have a stabilizing action by absorbing heavy metals from the slurry and meanwhile avoiding carbohydrate depolymerization by stabilization of glycoside bonds (García and Vidal 1984). On the other hand, DTPA sequestrates transition metals that support the decomposition of peroxide during the treatment (Wuorimaa et al. 2006).

Chemical characterization of the cellulosic pulps

The chemical composition of seaweeds varies with species, maturity, geographical location, seasonality, habitats, and environmental growing conditions (Mendis and Kim 2011). Seaweeds and marine plants have a high moisture content normally representing the 63–96% of the wet weight of the algae and it’s related to variations between species and their different thallus architecture (Baghel et al. 2021). The rest of the weight contains the organic matter (carbohydrates, lipids, and proteins) and ashes. ASH fraction is very variable being of 8.72% in C. nodosa (Kolsi et al. 2016), 7.60% in D. dichotoma (Ozgun and Turan 2015), 16.61% in L. variegata (Nithya et al. 2022) and 36-38.80% in A. taxiformis (McDermid and Stuercke 2003). D. dichotoma have a total lipid content under 10%, whereas L. variegata amount is below 5% (Gosch et al. 2012). Carbohydrates proportion in marine plants and seaweeds species vary depending on their classification: Ochrophyta (brown algae), range from 26.86–41.03%, Chlorophyta (green algae) 53.08–67.40% and Rhodophyta (red algae) 57.79–74.11% (Ahmad et al. 2016). Table 2 shows the averages of the two determinations carried out for the chemical characterization. The carbohydrates content represents the major fraction of the organic matter in seaweeds.

Table 2

Chemical characterization of the cellulosic pulps after NaOH-AQ and H₂O₂ treatments
X_T,X_t,X_P	ASH (%)	CEL (%)	EBE (%)	HOL (%)	HWS (%)	LIG (%)	YI (%)
0, 0, 0	7.77	85.12	1.89	45.90	36.82	7.16	56.22
1, 1, 1	7.43	88.60	1.43	55.98	30.00	5.32	44.81
-1, 1, 1	7.91	84.99	1.97	52.15	30.97	7.09	51.05
1, 1, -1	7.49	88.23	1.50	43.01	41.08	6.53	61.46
-1, 1, -1	8.26	84.74	1.99	41.78	39.57	8.23	67.97
1, -1, 1	7.62	87.52	1.64	53.67	30.90	6.31	45.83
-1, -1, 1	8.63	84.48	2.06	51.62	29.79	8.17	50.99
1, -1, -1	7.64	86.34	1.67	42.57	40.77	6.86	62.38
-1, -1, -1	8.84	84.34	2.05	41.46	38.13	8.97	68.41
0, 1, 0	7.69	86.06	1.78	46.47	37.62	6.90	56.50
0, -1, 0	7.88	85.03	1.94	45.66	37.42	7.35	57.06
0, 0, 1	7.72	85.57	1.85	51.93	32.40	6.71	47.93
0, 0, -1	7.83	85.11	1.91	43.32	39.82	6.99	63.78
1, 0, 0	7.51	87.91	1.56	46.19	39.36	6.06	53.88
-1, 0, 0	8.45	84.56	2.03	42.04	38.12	8.44	58.43

Table 2

Chemical characterization of the slurry after NaOH-AQ and H₂O₂ treatments.
X_T,X_t,X_P	ASH (%)	CEL (%)	EBE (%)	HOL (%)	HWS (%)	LIG (%)	YI (%)
0, 0, 0	7.77	85.12	1.89	45.90	36.82	7.16	56.22
1, 1, 1	7.43	88.60	1.43	55.98	30.00	5.32	44.81
-1, 1, 1	7.91	84.99	1.97	52.15	30.97	7.09	51.05
1, 1, -1	7.49	88.23	1.50	43.01	41.08	6.53	61.46
-1, 1, -1	8.26	84.74	1.99	41.78	39.57	8.23	67.97
1, -1, 1	7.62	87.52	1.64	53.67	30.90	6.31	45.83
-1, -1, 1	8.63	84.48	2.06	51.62	29.79	8.17	50.99
1, -1, -1	7.64	86.34	1.67	42.57	40.77	6.86	62.38
-1, -1, -1	8.84	84.34	2.05	41.46	38.13	8.97	68.41
0, 1, 0	7.69	86.06	1.78	46.47	37.62	6.90	56.50
0, -1, 0	7.88	85.03	1.94	45.66	37.42	7.35	57.06
0, 0, 1	7.72	85.57	1.85	51.93	32.40	6.71	47.93
0, 0, -1	7.83	85.11	1.91	43.32	39.82	6.99	63.78
1, 0, 0	7.51	87.91	1.56	46.19	39.36	6.06	53.88
-1, 0, 0	8.45	84.56	2.03	42.04	38.12	8.44	58.43
Relative standard deviation was never higher than 4%.

Relative standard deviation was never higher than 4%.

HWS characterization shows high values due to the hemicellulosic fraction solubilization. In marine biomass, composed in this case by C. nodosa and different seaweed species (mainly brown algae), it compasses the alkali-soluble polyuronic compounds analogous to pectin, e.g., alginic acid, water-soluble reserve carbohydrates analogous to starch, e.g., laminarin and water-soluble ethereal sulphates like mucilages, e.g., fucoidan. HOL percentages were very significant representing an average of 47%. However, these values are lower than those found for some other marine plants without treatment. P. oceanica can have HOL content ranging 55–57% and CEL levels of 32% (Tarchoun et al. 2019). The Cymodoceaceae family have proportions of ~ 30% soluble carbohydrates and hemicellulose, ~ 50% cellulose and ~ 20% lignin and insoluble polysaccharide residue content (Macreadie et al. 2017). Normally cellulose content in marine plants is higher among seaweeds, besides some studies revealed high cellulose content in brown algae, where crude fibre is the highest (21.66–34.71%) compared to red and green algae (Ahmad et al. 2016). Bogolitsyn et al. studied the content of defatted protein-polysaccharide complex of different brown algae. Results showed 56. 51.30 and 46% of cellulose content for Laminaria digitata, Laminaria saccharina and Ascophyllum nodosum respectively (Bogolitsyn et al. 2020). Another study from Ge et al. determined a 30% of cellulose from the floating residue by-product from the alginate extraction process from Laminaria japonica (Ge et al. 2011). Moreover, it is significant that more than 84% of the cellulose is present as CEL (predominant in algae and bacteria) (Horii et al. 1987), which indicates that it can be used in specialized applications in pharmaceutical industry as diluent, disintegrant, glidant and a binder in direct compression.

LIG in algae is controversial, some authors suggest there is less lignin or no lignin at all (Yanagisawa et al. 2013), since lignified cell walls are considered an evolutionary feature of terrestrial plants from aquatic ancestors some 475 million years ago (Boyce et al. 2004), among other authors that confirmed the presence of lignin in seaweeds. Alzate-Gaviria et al., confirmed the presence of aromatic polyphenol complex or lignin-like compounds in macroalgae biomass composed by Sargassum natans and Sargassum fluitans with values of 25.40 and 29.50% respectively (Alzate-Gaviria et al. 2021). Martone et al., discovered the presence of secondary walls and lignin like those of vascular plants within cells of the intertidal red alga Calliarthron cheilosporioides (Martone et al. 2009). However, the LIG in marine plants and seaweeds are lower compared wood raw materials and agricultural wastes (Moral et al. 2017), which is an advantage when it comes to revaluating cellulosic materials.

Modelling and optimization

To improve the HOL and CEL recovery, a central composite experimental design was carried out to see the effect of selective lignin removal. All experimental data were fitted into equations 2 to 8 obtaining linear regression models.

$$\:ASH=0.23{X}_{T}^{2}+0.21{X}_{T}{X}_{t}+0.11{X}_{P}{X}_{T}-0.86{X}_{T}-0.36{X}_{t}-0.15{X}_{P}+7.78\:\:\:\left(2\right)$$

$$\:CEL=0.26{X}_{T}^{2}+0.13{X}_{T}{X}_{t}+0.89{X}_{T}+0.28{X}_{t}+0.14{X}_{P}+85.38\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$

$$\:EBE=-\:0.20{X}_{T}^{2}-0.10{X}_{T}{X}_{t}´-0.93{X}_{T}-0.28{X}_{t}-0.07{X}_{P}+1.87\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$

$$\:HOL=0.25{X}_{P}^{2}+0.21{X}_{T}+0.92{X}_{P}+45.25\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)$$

$$\:HWS=-0.30{X}_{P}^{2}-0.93{X}_{P}+37.87\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$

$$\:LIG=-0.85{X}_{T}-0.31{X}_{t}-0.355{X}_{P}+7.14\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(7\right)$$

$$\:YI=0.04{X}_{t}^{2}-0.32{X}_{T}-0.03{X}_{t}-0.94{X}_{P}+56.05\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(8\right)$$

ASH and EBE are negatively affected by temperature, time and H₂O₂ concentration due to harder conditions that help to remove remaining minerals and lipids respectively, allowing thus to recover more purified HOL and CEL. HWS was only negatively affected by the H₂O₂ concentration related to the removal of the hemicellulosic fraction by the oxidative reaction (Thi Thuy Van et al. 2022). LIG was inversely affected by temperature, time and H₂O₂ concentration which means a lower content in lignin like compounds by increasing the operational factors. This step helps to reduce the cellulose’s impurities, hemicellulose, and lignin like compounds by a depolymerization process. HOL recovery was positively affected by temperature and H₂O₂ concentration. The harshness conditions of H₂O₂ allowed to obtain HOL with a higher CEL yield because of the isolation of the cellulosic fraction and the removal of the lignin like compounds. CEL content shows proportional relation with temperature, time and H₂O₂ concentration, with higher dependency with temperature. In other terms, higher temperature, time and H₂O₂ concentration allow to recover a carbohydrate fraction that is richer in CEL. YI is negatively affected mainly by temperature and peroxide concentration, playing reaction time a minor role; by increasing the temperature and H₂O₂ charge, the YI decreases.

Table 3 summarizes the results of analysis of variance (ANOVA) just for F-Snedecor value, probability value and standard error of estimate (Std. error) of the predictor variables. Coefficients where accepted when p < 0.05. The highest F value was given by the temperature variable in ASH, CEL, EBE and LIG, being the highest (1484.33) in EBE. Linear temperature predictor was the most influencing parameter in these regressions. For HOL, HWS and YI the setting with major effect was the H₂O₂ concentration, being in HWS the only linear and quadratic predictor with impact.

Table 3

ANOVA of linear, quadratic and interaction of the independent variables.
Variance analysis		X_T	X_t	X_P	X_T²	X_t²	X_P²	X_TX_t	X_PX_T
ASH	F-Snedecor	551.17	95.34	16.10	37.96	---	---	32.80	8.20
	Probability	< 0.001	< 0.001	0.004	< 0.001	---	---	< 0.001	0.02
	Std. error	0.02	0.02	0.02	0.03	---	---	0.02	0.02
CEL	F-Snedecor	421.69	42.37	10.12	36.84	---	---	9.32	---
	Probability	< 0.001	< 0.001	0.01	< 0.001	---	---	0.01	---
	Std. error	0.08	0.08	0.08	0.13	---	---	0.08	---
	F-Snedecor	1484.33	133.59	8.11	66.00	---	---	18.55	---
EBE	Probability	< 0.001	< 0.001	0.02	< 0.001	---	---	0.01	---
	Std. error	0.01	0.01	0.01	0.01	---	---	0.01	---
	F-Snedecor	12.42	---	229.80	16.87	---	---	---	---
HOL	Probability	0.01	---	< 0.001	0.01	---	---	---	---
	Std. error	0.35	---	0.35	0.61	---	---	---	---
	F-Snedecor	---	---	185.20	---	---	19.17	---	---
HWS	Probability	---	---	< 0.001	---	---	< 0.001	---	---
	Std. error	---	---	0.33	---	---	0.58	---	---
	F-Snedecor	151.10	20.20	24.82	---	---	---	---	---
LIG	Probability	< 0.001	< 0.001	< 0.001	---	---	---	---	---
	Std. error	0.08	0.08	0.08	---	---	---	---	---
	F-Snedecor	415.00	4.24	3555.41	---	6.10	---	---	---
YI	Probability	< 0.001	0.07	< 0.001	---	0.03	---	---	---
	Std. error	0.14	0.14	0.14	---	0.24	---	---	---

Table 4 collects the correlation coefficient (R), coefficient of determination (R²), adjusted coefficient of determination (R²-adjusted), standard error of estimate (Std. error), Constant Variance Test (Variance) and Normality Test Shapiro-Wilk (Normality).

Table 4

Statistical parameters values
Equation	R	R²	R²-adjusted	Std. error	Variance	Normality
ASH (2)	0.99	0.99	0.98	0.06	P = 0.81 (Passed)	P = 0.94 (Passed)
CEL (3)	0.99	0.98	0.97	0.24	P = 0.93 (Passed)	P = 0.74 (Passed)
EBE (4)	0.99	0.99	0.99	0.02	P = 0.36 (Passed)	P = 0.44 (Passed)
HOL (5)	0.98	0.96	0.95	1.11	P = 0.79 (Passed)	P = 0.99 (Passed)
HWS (6)	0.97	0.95	0.94	1.05	P = 0.56 (Passed)	P = 0.77 (Passed)
LIG (7)	0.97	0.95	0.93	0.25	P = 0.84 (Passed)	P = 0.49 (Passed)
YI (8)	0.99	0.99	0.99	0.44	P = 0.51 (Passed)	P = 0.32 (Passed)

The resultant second-order polynomial models adequately represented the experimental data with the coefficient of determination ranging from 0.95 to 0.99, being high enough for fitting the model to the experimental data. All obtained equations passed the Constant Variance Test and the Normality Test Shapiro-Wilk, assuming that the linear regressions present heteroscedasticity and are well-modelled by a normal distribution.

3D response surface plots of the effect of interaction between the more predominant factors on dependant variables HOL, CEL and LIG are showed in Fig. 2.

The response surface plots showed no local minima or maxima around the central point for each of the responses and under the range of conditions tested because their individual optimization lies in the star points of the central composite design. Nonetheless, when taking several responses into account, the complexity notoriously increases. The optimal parameters for maximal LIG removal (5.32%) were 70ºC, 90 minutes and 6% w/w H₂O₂ concentration. They lead to higher HOL recovery (55.98%) with more proportion of CEL (88.60%), compared to milder conditions where LIG value was 8.97% and HOL recovery 41.46%, under 50 ºC, 30 minutes and 1% w/w H₂O₂ concentration.

The multiple programming method of More and Toraldo (Moré and Toraldo 1989) was used to identify the optimal values of the independent variables by providing the optimal operational conditions for the cellulose extraction. Table 5 shows the results for each optimal predicted dependent variable to obtain the values of the operational conditions for obtain the best response by the modelized equations.

Table 5

Operating conditions for obtain predicted optimal values of dependent variables
Content (%)	Predicted optimal values	Operating conditions			Normalized values
Content (%)	Predicted optimal values	T (ºC)	t (min)	[H₂O₂] (%)	X_T	X_t	X_P
ASH	7.46	70	90	6	1	1	1
CEL	88.71	70	90	6	1	1	1
EBE	1.45	70	90	6	1	1	1
HOL	54.31	70	30	6	1	-1	1
HWS	30.81	70	30	6	1	-1	1
LIG	5.40	70	90	6	1	1	1
YI	68.12	50	30	1	-1	-1	-1

Figure 3 displays multiresponse contour plots for the three possible combinations of independent variables (plotted with Minitab® Analytical Software). White areas correspond to the combination of values that results in a product with a determinate composition. In this case, the elapsed independent variable is kept at its intermediate value. For ASH, CEL, EBE and LIG, the predicted optimal responses are obtained under the maximal operational conditions tested. The optimal cellulose recovery under these conditions will be of 88.71% and the higher delignification rate of 5.40%. By increasing the harshness of the conditions these rates could probably be augmented, but always in expenses of decreasing the YI, which is not always positive as this variable is optimized by milder operational conditions. In contrast, the HOL and HWS recovery are optimized with the higher temperature and H₂O₂ concentration and the lowest time setting.

Table 6 summarizes the deviations of the experimental values and the predicted optimal ones. As can we see, the experimental optimal values are well correlated with the predicted ones as Cook's Distance and Leverage values are acceptable. The responses to the considered optimal operational conditions are not influencing the obtained results by none of the selected dependent variables and cannot be considered outliers, giving therefore validity to the model. Table 7 represents the predicted optimized values of the dependant variables with different operational conditions under three different analysed cases: A, B and C.

Table 6

Experimental optimal values and their deviations from the predicted optimums
Content (%)	Experimental optimal values	Predicted optimal values	Std. residual	Cook's Distance	Leverage
ASH	7.43	7.46	− 0.51	0.19	0.65
CEL	88.60	88.71	− 0.46	0.08	0.53
EBE	1.43	1.45	− 0.81	0.25	0.53
HOL	55.98	54.31	− 0.57	0.05	0.30
HWS	29.79	30.81	0.08	0.01	0.20
LIG	5.32	5.40	− 0.32	0.02	0.37
YI	68.41	68.12	0.65	0.09	0.40

Std. residual: standardized residual. Relative standard deviation was never higher than

Table 7

Optimization of responses under different tested operational conditions
	Content (%)	Predicted	Residual	Std. residual	Cook's Dist.	Leverage
Case A	ASH	7.46	-0.03	− 0.51	0.19	0.65
	CEL	88.71	-0.11	− 0.46	0.08	0.53
	EBE	1.45	− 0.02	− 0.81	0.25	0.53
	HOL	54.31	1.67	1.51	0.35	0.30
	HWS	30.81	− 0.81	− 0.77	0.06	0.20
	LIG	5.40	− 0.08	− 0.32	0.02	0.37
	YI	45.17	− 0.36	− 0.81	0.15	0.40
Case B	ASH	7.59	0.03	0.57	0.25	0.65
	CEL	87.21	0.31	1.29	0.65	0.53
	EBE	1.64	− 0.00	− 0.04	0.00	0.53
	HOL	54.31	− 0.64	− 0.57	0.05	0.30
	HWS	30.81	0.09	0.08	0.00	0.20
	LIG	6.12	0.19	0.76	0.13	0.37
	YI	45.75	0.08	0.19	0.01	0.40
Case C	ASH	8.86	− 0.02	− 0.27	0.06	0.65
	CEL	84.15	0.19	0.80	0.25	0.53
	EBE	2.08	− 0.03	− 1.44	0.81	0.53
	HOL	41.19	0.27	0.24	0.01	0.30
	HWS	39.87	− 1.74	− 1.66	0.29	0.20
	LIG	8.88	0.09	0.36	0.03	0.37
	YI	68.12	0.29	0.65	0.09	0.40

Std. residual: standardized residual. Relative standard deviation was never higher than 4%.

Case A) Optimization of ASH, CEL, EBE and LIG: Temperature 70 ºC; time 90 min; [H₂O₂] 6%. Under harsher experimental conditions the cellulose extraction gives the maximum response. The contour plot corresponding to these variables at maximum time can be seen in Fig. 4. The white region corresponds to combinations of H₂O₂ and temperature values that result in LIG values below 6%, hydrophobic extractives below 1.80%, ASH content below 7.40%, and CEL proportions above 87%. LIG, EBE, and minerals are minimized. This scenario is the most interesting when CEL extraction is the main purpose of the extraction process, being able to reach an optimum value of 88.71%. The high regenerated CEL content is comparable to chlorine delignified sugarcane bagasse (89.33%) cooking at 205 ºC (Phinichka and Kaenthong 2018) and Kraft softwood (86.60%) and hardwood (84.70%) (Iller et al. 2002). This material could have applications like this obtained from terrestrial feedstocks in traditional extraction processes. It can be applied for paper production and manufacture cellulose-derived products, such as cellulose ethers, cellulose esters and regenerated fibres or films (Sixta 2006).

Case B) Optimization of HOL and HWS: Temperature 70 ºC; time 30 min; [H₂O₂] 6%. The minimum time setting of 30 minutes allowed to get the optimal values for HOL and HWS recovery, giving the same values for the responses then in Case A but consuming less treatment time. The extraction process is characterized by the mechanical damage, thermal and chemical degradation that cellulosic fibres suffer under the reaction conditions. Milder operational parameters allow to reduce the fibres decomposition. In the HOL fraction the cellulose fibrils are longer, facilitating the alignment of the fibres and the determination of their orientation (Yang and Berglund 2021). Holocellulosic fibres have being tested as a wood fibre reinforcement material. These fibres present higher intrinsic strength, ductility and mechanical integrity being suitable for load-bearing applications (Forsberg et al. 2022).

Case C) Optimization of YI: Temperature 50 ºC; time 30 min; [H₂O₂] 1%. Milder operating conditions allowed to obtain higher YI but reduces the cellulose and HOL content. Besides a CEL content of 84.15% is more than suitable for paper industry pulps, the HOL content of 41.19% is too low for compete with woody plants and agricultural residues. The HWS fraction of the slurry was higher than the obtained under the above-mentioned conditions with a content of 39.87%. This fraction constituted mainly by soluble carbohydrates that could be refined for obtaining added-value compounds present in brown seaweeds like alginates, laminarins and fucoidans that can be used for pharmaceutical and nutraceutical purposes or for production of biofertilizer, food or bioethanol (Matos et al. 2021).

Depending on the expected revalorization outcome, the modelling of the extraction process by multiple linear regression analysis and optimization methods allow to obtain materials with different composition and characteristics. The tidal waste chemical composition can be compared with other feedstocks employed as cellulosic material source, like agricultural residues and woody sources, obtained under similar extraction conditions (Table 8). This comparison under similar conditions can give an idea of the revalorization possibilities for the tidal residue.

Table 8

Chemical composition for different raw materials under NaOH -AQ (S1)-and AHP (S2) stages
ASH (%)	CEL (%)	EBE (%)	HOL (%)	HWS (%)	LIG (%)	YI (%)	Reference
Tidal waste S1: [NaOH] = 4%, [AQ] = 0.50%, L/S = 20, T = 130 ºC, t = 120 min. S2: [H₂O₂] = 6%, L/S = 10, T = 70 ºC, t = 30 min.							This study
7.59	87.21	1.64	54.31	30.81	6.12	45.75
Arenga pinnata S1: [NaOH] = 10%, [AQ] = 0%, L/S = 10, T = 150 ºC, t = 120 min. S2: [H₂O₂] = 5%, L/S = 10, T = 60 ºC, t = 90 min.							Fitriana et al. 2020
0.18	---	---	94.48	---	5.34	---
Bamboo chips S1: [NaOH] = 8%, [AQ] = 0%, L/S = 10, T = 100 ºC, t = 180 min. S2: [H₂O₂] = 6%, L/S = 10, T = 75 ºC, t = 180 min.							Yuan et al. 2018
---	---	---	71.50	---	13.80	58.30
Brewer’s spent grain S1: [NaOH] = 2%, [AQ] = 0%, L/S = 20, T = 120 ºC, t = 90 min. S2: [H₂O₂] = 5%, L/S = 20, T = 70 ºC, t = 40 min.							Mussatto et al. 2008
1.40	---	---	74.50	---	12.60	78.80
Grass waste S1: [NaOH] = 1%, [AQ] = 0%, L/S = 4, T = 55 ºC, t = 360 min. S2: [H₂O₂] = 4%, L/S = 4, T = 55 ºC, t = 360 min.							Macreadie et al. 2017
---	---	---	71.60	---	8.20	54.30
Sago Frond S1: [NaOH] = 10%, [AQ] = 0%, L/S = 20, T = 100 ºC, t = 120 min. S2: [H₂O₂] = 5.43%, L/S = 2, T = 95 ºC, t = 60 min.							Arnata et al. 2019
---	---	---	62.86	---	9.16	38.50
Sugarcane Straw S1: [NaOH] = 20.60%, [AQ] = 0.15%, L/S = 12, T = 160 ºC, t = 210 min.+ S2: [H₂O₂] = 9.40%, L/S = 20, T = 60 ºC, t = 60 min.							Costa el al. 2013
---	---	---	90.00	---	0.50	38.00
Wheat straw S1: [NaOH] = 1.25%, [AQ] = 0%, L/S = 10, T = 30 ºC, t = 120 min. S2: [H₂O₂] = 6%, L/S = 10, T = 50 ºC, t = 180 min.							Yuan et al. 2018
---	---	---	78.80	---	18.10	77.60

Relative standard deviation was never higher than 4%.

Despite the lack of information, the YI value could be compared, being the tidal waste YI (45.75%) between the range of terrestrial cellulosic materials (38.00-78.80%). The other comparable response with references is HOL, which showed a lower value (54.31%) compared with terrestrial sources (62.86–94.48%). Due to the characterization method here employed, it can be assumed that the HOL content of the tidal waste can be considered as mostly cellulose as the main hemicellulosic portion is almost comprised in the HWS characterization. This is since seaweeds and marine species hemicellulosic fraction is alkali-soluble and water-soluble. On the other hand, LIG was one of the lowest (6.19%) compared to the feedstocks after the AHP process (5.34–18.10%), which is beneficial as the recalcitrant nature of lingin increases the chemical consumption and the processing conditions (Hu et al. 2018). Moreover, the employed time in the H₂O₂ stage of this study (30 min) was the lowest compared to terrestrial feedstocks (40–360 min).

Marine beach cast material is a conforming mixture of different species of seaweeds and marine plant species, composition is thus dynamic and heterogeneous. Besides this originally drawback, the modelling and multiple response optimization of the AHP treatment demonstrated the potential of cellulose extraction from this alternative and sustainable feedstock.

The optimal tested operating conditions for the extraction process was 70 ºC, 30 minutes and 6% concentration of H₂O₂, giving a value of 87.21% CEL and 54.31% HOL. This regenerated cellulose source can be applied for paper industry and manufacture of cellulose-derived products. The HWS fraction of 30.81% should not be neglected and can be potentially refined to obtain value added compounds, allowing the possibility for adapt the extraction into an integrated biorefinery process.

Sustainable cellulose from beach wrack was isolated through minimal environmental impact techniques (NaOH-AQ and AHP) representing an excellent opportunity to valorize tidal waste and incorporate it into a circular economical process to supply the growing cellulose market. This cellulose can contribute to cover the global demand and meanwhile mitigate the terrestrial plant stock that come from deforestation and arable land. The extraction and processing of cellulose from algae can contribute to the sustainable development of coastal communities providing new employment opportunities and diversification of sources of income.

Funding

Funding for open access publishing: Universidad Pablo de Olavide/CBUA. This publication has been co-fnanced by the European Regional Development Fund (FEDER) and by the Ministry of Economy, Knowledge, Business and University, of the Junta de Andalucía, within the framework of the FEDER Andalusia operational program 2014–2020. Specifc objective 1.2.3 “Promotion and generation of frontier knowledge and knowledge oriented to the challenges of society, development of emerging technologies”, within the framework of the reference research project UPO-1381251. FEDER co-fnancing rate 80%.

Competing interests

Authors declare that they have no known competing interests or personal relationships that could have appeared to infuence the work reported in this paper.

Ethical approval

Not applicable.

Author Contribution

All authors made substantial contributions to the conception of the work, the acquisition and interpretation of data, and writing. All authors approve the manuscript. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Acknowledgements

This publication has been co-financed by the European Regional Development Fund (FEDER) and by the Ministry of Economy, Knowledge, Business and University, of the Junta de Andalucía.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abdel-Hakim A, Mouradb RM (2023) Nanocellulose and its polymer composites: preparation, characterization, and applications. Russ Chem Rev. https://doi.org/10.57634/RCR5076et
Aguado R, Moral A, Tijero A (2018) Cationic fibers from crop residues: Making waste more appealing for papermaking. J Clean Prod. https://doi.org/10.1016/j.jclepro.2017.11.053
Ahmad F, Sulaiman MR, Saimon W, Yee CF, Matanjun P (2016) Proximate compositions and total phenolic contents of selected edible seaweed from Semporna, Sabah, Malaysia. Borneo Sci. 31:74–83
Alzate-Gaviria L, Domínguez-Maldonado J, Chablé-Villacís R et al (2021) Presence of Polyphenols Complex Aromatic “Lignin” in Sargassum spp. from Mexican Caribbean. J Mar Sci Eng. https://doi.org/10.3390/jmse9010006
Arnata IW, Fahma F, Richana N, Sunarti TC (2019) Cellulose Production from Sago Frond with Alkaline Delignification and Bleaching on Various Types of Bleach Agents. Orient J Chem. https://doi.org/10.13005/ojc/35Specialissue102
Baghel RS, Reddy CRK, Singh RP (2021) Seaweed-based cellulose: Applications, and future perspectives. Carbohydr Polym. https://doi.org/10.1016/j.carbpol.2021.118241
Bajpai P (2012) Environmentally Benign Approaches for Pulp Bleaching. Elsevier, Netherlands.
Bogaert KA, Delva S, De Clerck O (2020) Concise review of the genus Dictyota JV Lamouroux. J Appl Phycol. https://doi.org/10.1007/s10811-020-02121-4
Bogolitsyn K, Parshina A, Aleshina L (2020) Structural features of brown algae cellulose. Cellulose. https://doi.org/10.1007/s10570-020-03485-z
Boyce CK, Zwieniecki MA, Cody GD, Jacobsen C, Wirick S, Knoll AH, Holbrook NM (2004) Evolution of xylem lignification and hydrogel transport regulation. PNAS. https://doi.org/10.1073/pnas.0408024101
Cabrera E, Muñoz MJ, Martín R, Caro I, Curbelo C, Díaz AB (2014) Alkaline and alkaline peroxide pretreatments at mild temperature to enhance enzymatic hydrolysis of rice hulls and straw. Bioresour Technol. https://doi.org/10.1016/j.biortech.2014.05.103
Campos-Martin JM, Blanco‐Brieva G, Fierro JLG (2006) Hydrogen peroxide synthesis: an outlook beyond the anthraquinone process. Angew Chem Int Ed Engl. https://doi.org/10.1002/anie.200503779
Costa SM, Mazzola PG, Silva JCAR, Pahl R, Pessoa JrA, Costa SA (2013) Use of sugarcane straw as a source of cellulose for textile fiber production. Ind Crops Prod. https://doi.org/10.1016/j.indcrop.2012.05.028
Datawheel (2023) The Observatory of Economic Complexity. https://oec.world/es Accessed 15 October 2024.
Dixon WJ (1988) BMDP Statistical Software Manual: To Accompany the 1988 Software Release. University of California Press, Oakland
Du H, Liu C, Zhang M, Kong Q, Li B, Xian M (2018) Preparation and industrialization status of nanocellulose. Prog Chem. https://doi.org/10.7536/PC170830
European Parliament (2018) Directive 2018/852 of the European Parliament and of the Council amending Directive 94/62/EC on packaging and packaging waste. http://data.europa.eu/eli/dir/2018/852/oj
Fitriana NE, Suwanto A, Jatmiko TH, Mursiti S, Prasetyo DJ (2020) Cellulose extraction from sugar palm (Arenga pinnata) fibre by alkaline and peroxide treatments. Earth Environ. https://doi.org/10.1088/1755-1315/462/1/012053
Forsberg DCR, Westin PO, Li L, Svedberg A, Grundberg H, Berglund LA (2022) A method for chemical and physical modification of oriented pulp fibre sheets. Cellulose. https://doi.org/10.1007/s10570-022-04706-3
García JA, Vidal T (1984) Blanqueo de pastas en la industria papelera. UPC, ETSII de Terrassa, Terrassa.
Ge L, Wang P, Mou H (2011) Study on saccharification techniques of seaweed wastes for the transformation of ethanol. Renew Energy. https://doi.org/10.1016/j.renene.2010.06.001
Gosch BJ, Magnusson M, Paul NA, De Nys R (2012) Total lipid and fatty acid composition of seaweeds for the selection of species for oil-based biofuel and bioproducts. GCB Bioenergy. https://doi.org/10.1111/j.1757-1707.2012.01175.x
Hart PW, Rudie AW (2014) Anthraquinone-A review of the rise and fall of a pulping catalyst. Tappi J. 13:23–31
Head of State (2007) Law 42/2007 of 13 December on Natural Heritage and Biodiversity. BOE. https://www.boe.es/eli/es/l/2007/12/13/42/con
Horii F, Hirai A, Kitamaru R (1987) CP/MAS carbon-13 NMR spectra of the crystalline components of native celluloses. Macromolecules. https://doi.org/10.1021/MA00175A012
Hu J, Zhang Q, Lee DJ (2018) Kraft lignin biorefinery: A perspective. Bioresour Technol. https://doi.org/10.1016/j.biortech.2017.08.169
Hubbel CA, Ragauskas AJ (2010) Effect of acid-chlorite delignification on cellulose degree of polymerization. Bioresour Technol. https://doi.org/10.1016/j.biortech.2010.04.029
Hyndes GA, Berdan EL, Duarte C et al (2022) The role of inputs of marine wrack and carrion in sandy-beach ecosystems: a global review. Biol Rev. https://doi.org/10.1111/brv.12886
Iller E, Kukiełka A, Stupińska H, Mikołajczyk W (2002) Electron-beam stimulation of the reactivity of cellulose pulps for production of derivatives. Radiat Phys Chem. https://doi.org/10.1016/S0969-806X(01)00646-6
Kolsi RBA, Fakhfakh J, Krichen F et al (2016) Structural characterization and functional properties of antihypertensive Cymodocea nodosa sulfated polysaccharide. Carbohydr Polym. https://doi.org/10.1016/j.carbpol.2016.05.098
Liu Z, Li L, Liu C, Xu A (2018) Pretreatment of corn straw using the alkaline solution of ionic liquids. Bioresour Technol. https://doi.org/10.1016/j.biortech.2018.03.117
Liu T, Williams DL, Pattathil S, Li M, Hahn MG, Hodge DB (2014) Coupling alkaline pre-extraction with alkaline-oxidative post-treatment of corn stover to enhance enzymatic hydrolysis and fermentability. Biotechnol Biofuels. https://doi.org/10.1186/1754-6834-7-48
Macreadie PI, Trevathan-Tackett SM, Baldock JA, Kelleway JJ (2017) Converting beach-cast seagrass wrack into biochar: A climate-friendly solution to a coastal problem. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2016.09.021
Martone PT, Estevez JM, Lu F, Ruel K, Denny MW, Somerville C, Ralph J (2009) Discovery of lignin in seaweed reveals convergent evolution of cell-wall architecture. Curr Biol. https://doi.org/10.1016/j.cub.2008.12.031
Matos GS, Pereira SG, Genisheva ZA, Gomes AM, Teixeira JA, Rocha CM (2021) Advances in Extraction Methods to Recover Added-Value Compounds from Seaweeds: Sustainability and Functionality. Foods. https://doi.org/10.3390/foods10030516
McDermid KJ, Stuercke B (2003) Nutritional composition of edible Hawaiian seaweeds. J Appl Phycol. https://doi.org/10.1023/B:JAPH.0000004345.31686.7f
Mendis E, Kim SK (2011) Present and future prospects of seaweeds in developing functional foods. Adv Food Nutr Res. https://doi.org/10.1016/B978-0-12-387669-0.00001-6
Milledge JJ, Nielsen BV, Bailey D (2016) High-value products from macroalgae: the potential uses of the invasive brown seaweed, Sargassum muticum. Rev Environ Sci Biotechnol. https://doi.org/10.1007/s11157-015-9381-7
Ministry of Environment, Rural and Marine (2011) RD 139/2011 of 4 February for the development of the List of Wild Species in Regime of Special Protection and of the Spanish Catalogue of Threatened Species. BOE. https://www.boe.es/eli/es/rd/2011/02/04/139/con
Montgomery DC (2018) Design and Analysis of Experiments. Grupo Editorial Iberoamericana, México.
Moral A, Aguado R, Mutjé P, Tijero A (2016) Papermaking potential of Citrus sinensis trimmings using organosolv pulping, chlorine-free bleaching and refining. J Clean Prod. https://doi.org/10.1016/j.jclepro.2015.09.008
Moral A, Aguado R, Tijero A, Tarrés Q, Delgado-Aguilar M, Mutjé P (2017) High-yield pulp from Brassica napus to manufacture packaging paper. Bioresources. https://doi.org/279.10.15376/biores.12.2.2792-2804
Moré JJ, Toraldo G (1989) Algorithms for bound constrained quadratic programming problems. Numer Math. https://doi.org/10.1007/BF01396045
Mussatto SI, Rocha GJ, Roberto IC (2008) Hydrogen peroxide bleaching of cellulose pulps obtained from brewer’s spent grain. Cellulose. https://doi.org/10.1007/s10570-008-9198-4
Nithya P, Archana L, Kokila P, Viji M, Murugan P, Maruthupandian A (2022) The Nutritional Assessment on Brown Macro algae Lobophora variegata from GOMBR, Tamil Nadu, India. J Adv Appl Sci Res. https://doi.org/10.46947/joaasr422022157
Ozgun S, Turan F (2015) Biochemical composition of some brown algae from Iskenderun Bay, the northeastern Mediterranean coast of Turkey. J Black Sea/Medit Environ. 21, 2, 125–134.
Pavón-Salas N, Herrera R, Hernández-Guerra A, Haroun R (2000) Distributional pattern of seagrasses in the Canary Islands (Central-East Atlantic Ocean). J Coast Res. 16,2, 329–335.
Percival EGV, Ross AG (1949) Marine algal cellulose. J Chem Soc. https://doi.org/10.1039/JR9490003041
Petrounias P, Giannakopoulou PP, Rogkala A et al (2023) Posidonia oceanica Balls (Egagropili) from Kefalonia Island Evaluated as Alternative Biomass Source for Green Energy. J Mar Sci Eng. https://doi.org/10.3390/jmse11040749
Phinichka N, Kaenthong S (2018) Regenerated cellulose from high alpha cellulose pulp of steam-exploded sugarcane bagasse. JMR&T. https://doi.org/10.1016/j.jmrt.2017.04.003
Robbe E, Woelfel J, Balčiūnas A, Schernewski G (2021) An impact assessment of beach wrack and litter on beach ecosystem services to support coastal management at the Baltic Sea. Environ Manag. https://doi.org/10.1007/s00267-021-01533-3
Sangil C, Martín-García L, Afonso-Carrillo J, Barquín J, Sansón M (2018) Halimeda incrassata (Bryopsidales, Chlorophyta) reaches the Canary Islands: mid-and deep-water meadows in the eastern subtropical Atlantic Ocean. Botanica Marina. https://doi.org/10.1515/bot-2017-0104
Singh J, Suhag M, Dhaka A (2015) Augmented digestion of lignocellulose by steam explosion, acid and alkaline pretreatment methods: a review. Carbohydr Polym. https://doi.org/10.1016/j.carbpol.2014.10.012
Sixta H (2006). Handbook of Pulp. John Wiley & Sons, Austria.
TAPPI (1999) T203 Alpha-, beta- and gamma-cellulose in pulp. T. Press.
TAPPI (2015) T222 om-15. Acid-insoluble lignin in Wood and pulp. T. Press.
TAPPI (2016) T211 om-16. Ash in Pulp, paper and paperboard: Combustion at 525 ºC. T. Press.
TAPPI (2017) T204 cm-17. Solvent extractives of Wood and pulp. T. Press.
TAPPI (2022) T207 cm-22. Water solubility of Wood and pulp. T. Press.
Tarchoun AF, Trache D, Klapötke TM (2019) Microcrystalline cellulose from Posidonia oceanica brown algae: Extraction and characterization. Int J Biol Macromol. https://doi.org/10.1016/j.ijbiomac.2019.07.176
Thi Thuy Van N, Gaspillo PA, Thanh HGT et al (2022) Cellulose from the banana stem: optimization of extraction by response surface methodology (RSM) and charaterization. Heliyon. https://doi.org/10.1016/j.heliyon.2022.e11845
Wuorimaa A, Jokela R, Aksela R (2006) Recent developments in the stabilization of hydrogen peroxide bleaching of pulps: An overview. Nord Pulp Paper Res J. https://doi.org/10.3183/npprj-2006-21-04-p435-443
Yanagisawa M, Kawai S, Murata K (2013) Strategies for the production of high concentrations of bioethanol from seaweeds: production of high concentrations of bioethanol from seaweeds. Bioengineered. https://doi.org/10.4161/bioe.23396
Yang X, Berglund LA (2021) Structural and ecofriendly holocellulose materials from wood: microscale fibers and nanoscale fibrils. Adv Mater. https://doi.org/10.1002/adma.202001118
Yuan Z, Wen Y, Li G (2018) Production of bioethanol and value-added compounds from wheat straw through combined alkaline/alkaline-peroxide pretreatment. Bioresour Technol. https://doi.org/10.1016/j.biortech.2018.03.044
Zanolla M, Altamirano M, De la Rosa J, Niell FX, Carmona R (2018) Size structure and dynamics of an invasive population of lineage 2 of Asparagopsis taxiformis (Florideophyceae) in the Alboran Sea. Phycological Res. https://doi.org/10.1111/pre.12189
Zárate R, Portillo E, Teixidó S et al (2020) Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia. Appl Sci. https://doi.org/10.3390/app10175831

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Revalorization of tidal waste as a sustainable cellulose source

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Introduction

Materials and methods

Tidal waste collection and species identification

Cellulose extraction

Chemical characterization of cellulose pulps

Modelling and optimization (dup: abstract ?)

Results and discussion

Species identification

Chemical characterization of the cellulosic pulps

Modelling and optimization

Conclusions

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Funding

Competing interests

Author Contribution

Acknowledgements

Data availability

References

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