Biomedical Engineering Reference
In-Depth Information
(XRD) patterns were recorded by a X'Pert Pro MPD X-ray diffractometer equipped with Cu
Kα radiation (λ= 0.154 nm). Fourier transform infrared spectroscopy (FTIR) was used to
examine any changes in the chemical structure of samples. A Nicolet 380 (Thermo electron
Instruments Co., Ltd., USA) was used to obtain the spectra of each sample. The rheological
behavior of sample was examined by DV-III+pro rheometer (Brookfield Engineering
Laboratories, Inc., USA) with SC4-34 spindle.
3. Results and discussion
3.1 Optimization of hydrolysis conditions for nanocellulose
The effect of process variables like ratio of NKC-9 to MCC, temperature and time on the
preparation of nanocellulose was investigated by means of response surface methodology,
Box-Behnken Design (BBD). Table 2. shows the coded value of the variables and the yield of
nanocellulose (response). The whole design consisted of 17 experimental points carried out
in random order. Five replicates at the centre of the design were used to estimate a pure
error sum of squares. The data obtained was analyzed by applying multiple regression
analysis method based on Eq. (1). The predicted response Y for nanocellulose yield was
obtained and shown as:
Y = 50.68 + 0.15X
1
- 1.11X
2
+ 1.67 X
3
+ 0.74X
1
X
2
- 0.35X
1
X
3
- 1.07X
2
X
3
-
- 3.85X
1
2
- 3.83X
2
2
- 5.17 X
3
2
(2)
In this equation, Y is the predicted response variable, i.e., the yield of nanocellulose (%), X
1
,
X
2
and X
3
are the independent variables in coded units, i.e., ratio of NKC-9 to MCC,
temperature and time, respectively.
The data obtained from Eq. (2) are significant. It is verified by F-value and the analysis of
variance (ANOVA) by fitting the data of all independent observations in response surface
quadratic model. The summary of the analysis of variance (ANOVA) of the results of the
quadratic model fitting are shown in Table 3. ANOVA is indispensable to testing the
significance and adequacy of the model. The corresponding variables would be more
significant if the absolute F-value becomes greater and the p-value becomes smaller (Chen et
al., 2010). The model F-value of 42.80 implies that the model is significant. There is only a
0.01% chance that a “model F-value” this large could occur due to noise. Value of “Prob > F”
less than 0.05 indicates that the model terms are significant. In this case, X
2
, X
3
, X
2
X
3
, X
1
2
, X
2
2
and X
3
2
are significant model terms. However, ratio of NKC-9 to MCC (X
1
) and interaction
terms (X
1
X
2
and X
1
X
3
) had a negative effect on Y.
The “Lack of fit F-value” of 4.79 implies that the lack of fit is not significant relative to the
pure error. There is a 8.23% chance that a “Lack of fit F-value” this large could occur due to
noise. The determination coefficient(R
2
), a measure of the goodness of fit of the model, was
very significant at the level of 98.22%, the model was unable to explain only 1.78% of the
total variations. The value of adjusted R
2
, was also very high at the level of 0.9592, indicating
high significance of the model.
The effect of hydrolysis conditions on the yield of nanocellulose is shown in Table 2 by the
coefficient of the second-order polynomials. To visualize and identify the type of
interactions between test variables, the two and three dimensional contour plots are shown
in Fig. 1, including ratio of NKC-9 to MCC and temperature, ratio of NKC-9 to MCC and
time, as well as temperature and time. The circular contour plots indicate that the interaction
