From Cellulose Nanospheres, Nanorods to Nanofibers: Various

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From Celluloses Nanospheres, Nanorod to Nanofibers: Various Aspect Ratios Induced Different Nucleation/Reinforcing Effect on Polylactic acid for Robust-barrier Food Packaging Hou-Yong Yu, Heng Zhang, Mei-Li Song, Ying Zhou, Juming Yao, and Qing-Qing Ni ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b09102 • Publication Date (Web): 24 Nov 2017 Downloaded from http://pubs.acs.org on November 29, 2017

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ACS Applied Materials & Interfaces

1

From Celluloses Nanospheres, Nanorod to Nanofibers: Various Aspect

2

Ratios Induced Different Nucleation/Reinforcing Effect on Polylactic acid

3

for Robust-barrier Food Packaging

4

Hou-Yong Yu †*, ‡, Heng Zhang†, Mei-Li Song†, Ying Zhou†, Juming Yao†, Qing-Qing Ni†

5 6 7

†

The Key Laboratory of Advanced Textile Materials and Manufacturing Technology of

8

Ministry of Education, National Engineering Lab for Textile Fiber Materials & Processing

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Technology, College of Materials and Textile, Zhejiang Sci-Tech University, Xiasha Higher

10 11 12

Education Park 2 Avenue-5, Hangzhou 310018, China. *E-mail: [email protected] ‡

State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Donghua

University, Shanghai 201620, China

13 14

ABSTRACT: The traditional approach toward improving crystallization rate, mechanical and

15

barrier property of poly(lactic acid) (PLA) is the incorporation of nanocelluloses (NCs).

16

Unfortunately, little study was focused on the influence of differences in NC morphology and

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dimension on the PLA property enhancement. Here we unveil the preparation of cellulose

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nanospheres (CNS), rod-like cellulose nanocrystals (CNC), and cellulose nanofibers (CNF)

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with different aspect ratios by HCOOH/HCl hydrolysis of lyocell fibers, microcrystalline

20

cellulose (MCC) and ginger fibers, respectively. All the NC surfaces were chemically

21

modified by Fischer esterification with hydrophobic formate groups to improve the NC

22

dispersion in PLA matrix. This study systematically compared CNS, CNC, and CNF as

23

reinforcing agents to induce different heterogeneous nucleation and reinforcing effects on the

24

properties of PLA. The incorporation of three NCs can greatly improve PLA crystallization

1 Environment ACS Paragon Plus

ACS Applied Materials & Interfaces 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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ability, thermal stability and mechanical strength of nanocomposites. At the same NC loading

2

level, the PLA/CNS showed the highest crystallinity (19.8 ± 0.4 %) with smaller spherulite

3

size (33 ± 1.5 µm), indicating CNS with the high specific surface area can induce stronger

4

heterogeneous nucleation effect on the PLA crystallization than CNC and CNF. Instead,

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compared to PLA, the PLA/CNF nanocomposites gave the largest Young’s modulus increase

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of 350 %, due to the larger aspect ratio/rigidity of CNF and their interlocking or percolation

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network caused by filler-matrix interfacial bonds. Furthermore, taking these factors of

8

hydrogen bonding interaction, increased crystallinity and interfacial tortuosity into accounts,

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the PLA/CNC nanocomposite films showed the best barrier property against water vapor and

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lowest migration levels in two liquid food simulates (well below 60 mg kg-1 for required

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overall migration in packaging) than CNS and CNF based films. This comparative study was

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very beneficial for selecting reasonable nanocelluloses as nucleation/reinforcing agents in

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robust-barrier packaging biomaterials with outstanding mechanical and thermal performances.

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KEYWORDS: cellulose nanospheres; cellulose nanocrystals; cellulose nanofibers;

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polylactic acid nanocomposites; nucleation effect; reinforcing effect

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1. INTRODUCTION

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With an immense awareness of environmental sustainability, fully biodegradable

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poly(lactic acid) (PLA) derived from renewable resources has received growing attention in

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packaging applications due to good physicochemical properties in comparison to conventional

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petroleum-based polymers1-5. However, the low crystallization rate, poor moisture barrier

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properties and high migration levels of pure PLA hinder its promising food packaging

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applications. Therefore, graphene oxide (GO)3,5, nanoclay6, carbon nanotubes7, graphene

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nanoplatelets (GN)8,9 and nanocelluloses (NCs)2,10-13 have been introduced to improve the

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crystallization ability and barrier properties of PLA. Although the above-mentioned properties


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can be solved to a certain extent, there are still vital shortcomings to sacrifice biodegradability

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of PLA nanocomposites with introducing above four inorganic nanofillers8, leading to a poor

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agreement with the concept of green sustainability. On the other hand, biodegradable

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nanocelluloses (NCs) with high rigidity (modulus of 150 GPa), and abundant hydroxyl groups

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(potential hydrogen bonds and surface functionalization for good dispersion into a polymer

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matrix) were incorporated into PLA matrix as reinforcing agents1,11-14. The nanocelluloses

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produced from various renewable resources show different sizes with various aspect ratios

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and reinforcing effects. It is well known that larger aspect ratio provides a stronger reinforcing

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effect on the water vapor barrier and mechanical properties15. Nevertheless, in most reported

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studies, the aspect ratios of used NCs (with rod-like shape) in the PLA matrix were about

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typically between 10 and 5013, almost no systematic comparison between different

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nanocelluloses with various aspect ratios and morphologies in PLA nanocomposites has been

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reported.

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Cellulose nanospheres (CNS), cellulose nanorods (CNC) and cellulose nanofibers (CNF)

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with different dimensions were used as typical three NCs with different aspect ratios 16-18. It is

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well known that the aspect ratio of these NCs played a key role in their reinforcing ability19,20.

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For example, the crystalline regions of CNF with high aspect ratio (>150) can make this

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material ideal as nanofiller in nanocomposites. The incorporation of CNF into biodegradable

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polymer will lead to enhancing the mechanical strength of the nanocomposite materials21. The

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mechanical properties of CNF could be optimized by adjusting the alignment of the

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nanofibrils. In general, more aligned fibrils have higher tensile strength and lower strain at

22

break, compared with lower aligned fibrils22. In addition, when the aspect ratio of NCs below

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10 cannot have any major advantages, compared to traditional micron-sized fillers, while

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aspect ratios larger than 50 can ensure highly efficient reinforcing effect17,23. Therefore, it is



1

very significant to simultaneously compare the reinforcing ability of CNF, CNC, and CNS in

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the PLA nanocomposites. The aim of this study was to compare the effect of nanocelluloses

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with various aspect ratios (1-100) on the microstructure, thermal, mechanical, barrier and

4

migration properties of PLA to focus on the contributions of their morphological differences

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(nanofibers vs nanorod vs nanospheres) and surface functionalization to the properties of PLA

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nanocomposites. To the best of our knowledge, there are scarcely any kinds of literatures to

7

evaluate experimentally measured Young’s moduli with theoretical predictions based on both

8

classical Ouali and Halpin-Kardos mechanical model, and establish the relationship among

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aspect ratios, nucleation effect or reinforcing effect, interfacial (hydrogen) bonds and final

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property enhancement mechanisms of PLA nanocomposites. This study is beneficial to

11

provide a theoretical and practical guidance for promoting the development of PLA or other

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biopolymer nanocomposite packaging containing nanocelluloses.

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2. Experimental SECTION

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2.1. Materials

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Commercial lyocell fibers (diameter: about 10 μm) without spin finishing were kindly

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supplied by Shanghai Lyocell Fibre Development Co., Ltd. Polylactic acid (PLA, Mn=1.

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0×105) was supplied by Bright China Industrial Co. Ltd as received without purification.

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Ginger fibers were kindly supplied by Jiangnan University. Commercial microcrystalline

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cellulose (MCC) (size: about 20 μm) were supplied by Shanghai Chemical Reagents

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(Shanghai, china), Formic acid (HCOOH) (88%), hydrochloric acid (HCl) (37%), ammonia

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solution (NH4OH) (25-28%), chloroform (99%), isooctane (99%) and ethanol (C2H5OH)

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(99.7%) were purchased from Hangzhou Mike Chemical Agents Co. Ltd., (Hangzhou, China).

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All the materials and reagents were used as received without further purification.


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2.2. Extraction of spherical nanocelluloses formats, cellulose nanocrystals, and cellulose

2

nanofibers

3

Spherical nanocelluloses formates (CNS) were prepared through hydrolysis of mixed acids

4

(90% Formic acid (HCOOH), and 10% hydrochloric acid (HCl)). In brief, commercial lyocell

5

fibers were firstly treated through scouring process with 1M NaOH aqueous solution for 3h in

6

order to remove the impurities and swell the amorphous regions of fibers. Finally, the treated

7

fibers were washed with deionized water and air-dried. After that 2.0 g of treated lyocell

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fibers were added into 120 ml of aqueous solution mixed acids hydrolysis of 90% (v/v)

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HCOOH/10%(v/v) HCl (3 M HCOOH and 3 M HCl) at 80 oC for 8 h under strongly

10

mechanical stirring. After cooling to room temperature, the resultant suspension was

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neutralized with 3M NH4OH aqueous solution and washed by successive centrifugations until

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the pH of the suspension was about 7. After 10 min of ultrasonic irradiation, the suspension

13

was freeze-dried for 48 h to remove the water and dry CNS was obtained. The detailed

14

extraction process of cellulose nanocrystals (CNC) was as follow: rod-like CNC were

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prepared through mixed acids of 90% (v/v) HCOOH/10%(v/v) HCl (3 M HCOOH and 3 M

16

HCl) hydrolysis of a cretin amount of commercial MCC were added to cretin amount mixed

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acids in one-pot process at 80 oC for 4 h24. Moreover, cellulose nanofibers (CNF) were

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prepared through mixed acids of 90% (v/v) HCOOH/10%(v/v) HCl hydrolysis (3M HCOOH

19

and 3 M HCl) of ginger fibers at 80 oC for 7 h and Fischer esterification reaction of accessible

20

hydroxyl groups in the one-pot process. After reaction time completed, all the resultant

21

suspensions were neutralized with 3M NH4OH solution, and washed by successive

22

centrifugations until the pH of suspensions were about 7. Then the suspensions were exposed

23

to ultrasonic mixing for 10 min. Finally, the resultant suspensions were freeze-dried for 48 h

24

for obtaining the dry CNS, CNC and CNF.

25

2.3. Preparation of the PLA/nanocellulose nanocomposites



1

PLA/CNS, PLA/CNC, and PLA/CNF nanocomposite films were prepared by solution casting

2

technique. Briefly, 10 wt% of PLA was dissolved in chloroform and stirred for 30 min at 60

3

o

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CNS, CNC and CNF based on the PLA weight) were added into chloroform solution under

5

ultrasonic mixing for 30 min, and the resultant solutions were stirred 24h at room temperature

6

until clear solution was obtained. Finally, PLA/CNS, PLA/CNC, and PLA/CNF

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nanocomposite films with the thickness of approximately 70–80 ± 3.5 μm were obtained on a

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glass slide via solution casting technique. The nanocomposite films were further dried under

9

vacuum at 40 oC overnight. Moreover, the morphologies and properties of nanocomposite

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films were characterized using (Field emission scanning electron microscopy (SEM), Fourier

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transform infrared spectroscopy (FTIR), Transmission electron micrographs (TEM), Atomic

12

force microscope (AFM), formate content measurement, X-ray diffraction measurements

13

(XRD), thermal stability, optical properties, etc), the characterizations were given in

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Supplementary Information.

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3. RESULTS AND DISCUSSION

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3.1 Morphologies and dimensions

C to make sure that the molecular chains of PLA were well entangled, after that (10 wt%

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Figure 1 shows SEM images of the lyocell fibers, MCC, ginger fiber and the products

18

after the acid hydrolysis of these raw materials. It was found that after acids hydrolysis of

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commercial lyocell fibers with a diameter of 10 μm (treated with NaOH), well dispersed CNS

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diameter of 27 ± 1.2 nm was obtained (Figure 1b), and this could be due to the formation of

21

carboxyl groups on the surface of CNS. The HCOOH/HCl hydrolysis decreased the size of

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the commercial MCC (diameter of about 20 μm), rod-like CNC with 16 ± 4 nm in diameter

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and 270±26 nm in length (the aspect ratio of about 16.8) were produced because of removal

24

of the amorphous cellulose, The CNC with rod-like morphology and geometric dimensions

25

were almost similar to data reported for CNs extracted from MCC25(Figure 1d). Moreover,


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the surface morphology of ginger fiber is shown in Figure 1e, suggesting that the fiber was

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closely cemented by a mass of sticky components. In addition, the CNF exhibited complex

3

and highly entangled network structure. From this micrograph, twisted/untwisted,

4

crimped/straight, and entangled/isolated nanofibers could be distinguished. The single CNF

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and fiber bundles were shown in Figure 1f. Moreover, the TEM image in Figure 1f (see insert

6

image) shows that the average width, and length of CNF were about (25 ± 5) nm and (2589 ±

7

79) nm, respectively. The aspect ratio of CNF was around (100 ± 3), and the aspect ratio of

8

CNF in nanocomposites might be changed due to the origin and the processing of CNF at

9

presence of CNF bundles. The HCOOH/HCl hydrolysis could leave the surface chemistry of

10

the CNF and its bundles relatively unchanged, leading to a negligible reduction of the

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hemicelluloses content and to a lower fibrillation degree of long CNF bundles with larger

12

diameters. Indeed, ultrasonic mixing process was a useful method to overcome the CNF–CNF

13

interaction and improve the dispersion of these CNF bundles.

14

15



1 2

Figure 1. SEM images of lyocell fibers (a), CNS (b), MCC (c), CNC (d), ginger fibers (e),

3

and CNF (f, Insert is TEM image of CNF).

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3.2 Chemical structures

5

Figure 2 shows the FT-IR spectra of lyocell fiber, CNS, MCC, CNC, ginger fiber and

6

CNF. Similar bands at 3270 cm-1 were assigned for O-H stretching vibrations and the out-of

7

plane O–H bending at 710 cm-1 can be observed for MCC, ginger fibers, CNC and CNF, both

8

CNC and CNF exhibited representative crystal Iβ structure26. In contrast, no above absorption

9

bands were found in the spectra of lyocell fiber and CNS. Instead, some characteristic bands

10

of cellulose II structure were observed. For example, bands located at 1056 and 1023 cm-1

11

were assigned to C-O-C stretching, while that at 894 cm-1 was ascribed to the motions of C-5

12

and C-6 atoms, respectively27. Moreover, compared to raw cellulosic materials, intensities of

13

the C-O-C stretching band (1113 cm-1) from pyranose and glucose ring skeletal vibrations for

14

all NCs were increased, indicating the increase of crystalline components26. In addition,

15

compared to raw materials, all the NCs exhibited a new peak at 1720 cm-1, ascribed to C=O

16

stretching of formate groups, and similar peak at 1736 cm-1 ascribing to C=O was also found

17

for the CNC prepared through hydrolysis of acetic/hydrochloric acid28. Meanwhile, through

18

the titration of formates, the formate contents in NCs were determined (Table 1). The formate

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contents of CNS, CNC, and CNF were 0.87 ± 0.18, 0.64 ± 0.14, and 0.54 ± 0.13 mmol/g,

20

respectively. Obviously, the CNF showed lowest formate contents, because the high aspect


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ratio of CNF made the hydroxyl groups be difficult to react with the carboxyl groups of

2

HCOOH.

3

4 5

Figure 2. FTIR spectra (a), formate contents (b) and magnified peak range of 900-1200 cm-1

6

(c) for lyocell fiber, CNS, MCC, CNC, ginger fiber and CNF.

7

3.3 Crystalline structure

8

According to three characteristic cellulose I reflections, the XRD patterns in Figure 3

9

further prove the cellulose I structure of MCC, CNC, ginger fibers, and CNF26. Conversely,

10

obvious changes appeared in diffraction patterns of lyocell fibers and CNS, i. e., the main

11

cellulose peak was split into two obvious peaks, locating at 2 θ = 20.1 ± 0.2 o (110) and 2 θ =

12

21.8 ± 0.2

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cellulose materials, nanocelluloses had broader diffraction peaks, further proving their smaller

14

average crystalline sizes, as shown in Table 1. Moreover, compared to lyocell fiber, CNS had

15

smaller crystalline size and higher crystallinity index, which was benefited from the efficient

o

(200), indicating the formation of cellulose II structure27. Compared to raw



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1

removal of amorphous regions in fibers. However, the crystallinity index of 83.9 ± 2.1 % for

2

CNS was slightly lower than 84.3 ± 2.1 % for CNC, probably due to the difference of raw

3

materials29. Meanwhile, it can be seen that the crystallinity indexes of ginger fibers and CNF

4

were far below than those of above materials, which was due to the more non-cellulose

5

compositions in ginger fibers and more amorphous regions in CNF related to dimensions,

6

leading to lower crystallinity. Actually, under the mild reaction conditions of heating and

7

stirring, HCOOH/HCl hydrolysis was beneficial to adequately attack only the amorphous

8

regions. Therefore, the obtained CNS and CNC showed relatively high crystallinity by using

9

this method.

10 11

Figure 3. The XRD patterns (a) and crystallinity index (b) of cellulose raw materials and

12

nanocellulose.

13

Table 1. The crystal size, crystallinity index (Xc), and formate group contents of cellulose

14

materials and nanocelluloses. Sample

D1Ī0 (nm)

D110 (nm)

D200 (nm)

Xc (%)

Formate content （mmol/g）

Lyocell fiber

5.3 ± 0.06

6.5 ± 0.05

6.7 ± 0.06

66.0 ± 1.6

/

CNS

4.5 ± 0.05

5.6 ± 0.06

5.9 ± 0.05

83.9 ± 2.1

0.87 ± 0.18

MCC

5.1 ± 0.06

6.7 ± 0.06

6.9 ± 0.06

74.4 ± 1.8

/


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CNC

4.0 ± 0.05

5.2 ± 0.04

6.2 ± 0.05

84.3 ± 2.1

0.64 ± 0.14

Ginger fiber

6.1 ± 0.05

5.1 ± 0.05

9.1 ± 0.08

31.8 ± 0.7

/

CNF

4.0 ± 0.04

3.9 ± 0.04

8.3 ± 0.06

50.5 ± 1.2

0.54 ± 0.13

1 2

3.4 Thermal stability

3

The thermal behavior of cellulose materials and CNS, CNC, and CNF was investigated

4

through thermogravimetric analysis (TGA) and derivative thermogravimetric (DTG). Figure

5

4(a and b) show the TGA and DTG curves of the raw cellulose materials, CNS, CNC, and

6

CNF at a heating rate of 10 oC min-1, and the thermal degradation onset temperature (T0) and

7

maximum degradation temperature(Tmax) are illustrated in Table 2. Figure 4a illustrates that

8

the CNS, CNC, and CNF have almost similar thermal degradation temperatures as raw

9

cellulose materials, indicating their thermal stability of original cellulose structures can be

10

kept under mild preparation conditions. Figure 4b shows that the thermal degradation of all

11

samples produced by one-step process, indicating one type of uniform crystals with narrow

12

size distribution, while ginger fibers was produced by a two-step process, this behavior can be

13

due to the presence of non-cellulose compositions. In Figure 4c, the CNF showed lowest

14

thermal stability compared with other NCs. The Tmax value of CNF was about 352.2±2.8 oC,

15

which was lower than those of CNS (358.0±2.8 oC) and CNC (366.5±2.9 oC). It is

16

suggested that the CNF with low crystallinity might considerably decrease the degradation

17

temperature. More significantly, the thermal stabilities of CNS and CNC were better than

18

those of previously reported NCs (Tmax of 150-298 oC)30. Generally, the poor thermal stability

19

of nanocelluloses as nanofillers in thermoplastic will further limit their some application of

20

melt extrusion/injection molding technology26. Figure 4d shows the plots of ln[ln(W0/WT)] vs.



Page 12 of 51

1

θ(Theta) of thermal degradation main stages for cellulose materials and NCs. Their average

2

apparent activation energies (Ea) can be calculated from the slopes of fitted lines by using

3

Horowitz and Metzger method as follows:

4

ln[ln(

W0 E )]  a 2 WT RTs

(1)

5

Where W0 is the initial weight of the polymer; WT is the residual weight of polymer at

6

temperature T; Ts is the temperature determined at 36.79% weight loss; θ is T - Ts. R is the gas

7

constant.

8

The results of Ea values are listed in Table 2. Generally, higher Ea represents faster

9

degradation rate31. Table 2 illustrates that compared to raw cellulose materials, NCs had

10

larger Ea values. Indeed, higher crystallinity index and the presence of formate groups

11

increased their thermal degradation temperature, thus the thermal degradation temperatures of

12

NCs occurred at a higher temperature, and this temperature was the factor to the increased

13

degradation rate of nanocelluloses.

14 15


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1 2 3

Figure 4. The TGA (a), DTG (b) curves, T0 and Tmax parameters (c), and plots of

4

ln[ln(W0/WT)] vs. θ (d) of raw cellulose materials and nanocelluloses.

5 6

Table 2. The thermal parameters of cellulose raw materials and nanocelluloses. Sample

Ea (kJ/mol)

T0 (oC)

Tmax (oC)

Lyocell fiber

314.2±2.8

355.8±2.8

110.60±0.77

CNS

323.1±2.9

358.0±2.8

248.30±1.73

MCC

327.3±2.9

346.1±2.7

68.42±0.47

CNC

345.3±3.1

366.5±2.9

280.74±1.96

Ginger fiber

308.0±2.7

349.7±2.7

84.36±0.58

CNF

302.0±2.7

352.2±2.8

136.84±0.95

7 8

3.5 Fractured surface of pure PLA and nanocomposites

9

The dispersion of nanocelluloses and their interfacial adhesion within PLA matrix are

10

investigated by SEM. Figure 5 shows the fractured surface of pure PLA and the

11

nanocomposites reinforced with CNS, CNC, and CNF. It shows a smooth fractured surface of

12

PLA, attributed to brittle nature of PLA (Figure 5a), while non-uniform fractured surfaces of



1

PLA nanocomposites was observed, suggesting a significant matrix deformation occurs after

2

adding the nanocelluloses. Figure 5b represents the fractured surface of PLA/CNS, and the

3

rough fractured surface was found by addition of 10 % nanoparticles. The fractured surfaces

4

of PLA/CNC and PLA/CNF exhibited similarly rod-like and fibrous bulges, as shown in

5

Figure 5c and d, respectively. TEM imags of the PLA/CNS and PLA/CNC nanocomposite

6

demonstrated a good dispersion of CNS and CNC within the PLA matrix, while the CNFs

7

with interlaced network structure were distributed on the PLA matrix (Figure 6). AFM

8

observations also confirmed above dispersion states (Figure S1).

9

10 11

Figure 5. Fractured surface of pure PLA (a), PLA/CNS (b), PLA/CNC (c), and PLA/CNF (d)

12

nanocomposite films.


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1

2 3 4

Figure 6. TEM images of PLA (a), PLA/CNS (b), PLA/CNC (c), PLA/CNF (d). 3.6 Hydrogen bonding interaction measured by FTIR spectra

5

Figure 7 showed all ATR-FTIR spectra of pure PLA and nanocomposites. All PLA-based

6

nanocomposites showed a similar characteristic peak at 1753 cm-1 ascribed to characteristic

7

ester C=O groups. It should be noted that the nanofillers contents and the interactions between

8

nanofillers and matrix could affect the distribution of ester C=O groups in crystalline and

9

amorphous regions of the nanocomposites. In order to divide the spectra of pure PLA and

10

nanocomposites with various NCs into three peaks, the curve-fitting was used for the range of

11

1650-1850 cm-1 in Figure 7b, i. e., peak I located at 1775 cm-1 ascribed to free C=O; peak II

12

located at 1753 cm-1 ascribed to C=O in amorphous regions; peak III located at 1734 cm-1

13

ascribed to C=O in crystalline regions32. Table 3 summarizes the detailed locations and

14

fractions of curve-fitted C=O peaks in the FTIR spectra of peak I, peak II, and peak III.



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1

Amorphous and free C=O components were in the majority in pure PLA. With the addition of

2

NCs, the rigid NCs in matrix restricted the motion of polymer chains, leading to decrease in

3

the ratio of amorphous C=O in the nanocomposites. The addition of CNC and CNF could

4

increase the proportion of the amorphous regions in the PLA. Although the rigid

5

nanocelluloses still restrict the motion of free C=O components, the nanofillers phase greatly

6

affected the existence of C=O in crystalline regions, and its fraction was gradually reduced in

7

the nanocomposites.

8

In order to determine the hydrogen bonding fraction (FH-CO), the carbonyl IR spectra from

9

1680 cm-1 to 1800 cm-1 were curve-fitted by using Gauss/Lorentz spectra function. As shown

10

in insert Figure 7c, the band about located at 1753 cm-1 can be ascribed to the hydrogen

11

bonded C=O groups33 and free C=O groups at 1765 cm-1. With the addition of NCs, compared

12

to pure PLA, the band location of hydrogen bonded components for nanocomposites

13

decreased to 1749 cm-1. This was ascribed to that the inter-molecule hydrogen bonding

14

interactions between CNS hydroxyls and PLA carbonyls weakened the polarity of ester C=O

15

groups, leading to the decrease of C=O band location34. However, the nanofiber

16

entanglements caused by the larger aspect ratio of CNF made the hydrogen bonding

17

interaction in PLA/CNF become slightly weak, therefore, the C=O band location shifted to

18

higher wavenumber. In addition, FH-CO value was determined to further analyze the change of

19

hydrogen bonding interaction in nanocomposites, and calculated by the reported equation34,35:

20

FH-CO 

AH / rH/a ( AH / rH/a  Aa )

(2)

21

where Aa and AH were peak area of the free and hydrogen bonded components, respectively,

22

and rH/a was specific absorbent ratio of above two bands. For the semi-quantitative

23

comparison purpose, rH/a was selected at 1.35 based on a minimum error in biopolymer/NC

24

nanocomposites26,34,36. Figure 7d presents FH-CO of nanocomposites. With the addition of NCs,


Page 17 of 51 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

the FH-CO of PLA/CNS was the largest, up to 0.21 ± 0.02; PLA/CNC was in the center, 0.19 ±

2

0.02; while PLA/CNF was the smallest, only 0.17 ± 0.01. From above, among all

3

nanocomposites, the strongest inter-molecule interaction appeared in the nanocomposites

4

containing CNS, due to the good dispersion and larger special area of CNS in a PLA matrix,

5

while the larger aspect ratio of CNF caused few agglomeration and nanofiber entanglements,

6

leading to the weakest inter-molecule interaction of the nanocomposites. In general, the good

7

dispersion of CNS and formation of more inter-molecule interactions were beneficial for

8

simultaneously improving the mechanical and thermal properties of nanocomposites26,36.

9

10 11 12

Figure 7. All ATR-FTIR spectra (a), curve-fitting in the range of 1650-1850 cm-1 (b),

13

carbonyl stretching region (νC=O) in FTIR spectra (c), hydrogen bonding fractions (d).

14 15

Table 3. Location and fraction of curve-fitting peaks for C=O absorption in the FTIR spectra

16

of pure PLA and its nanocomposites.



Sample

Peak Ia location (cm-1)

Page 18 of 51

Peak IIa Fraction

location

Peak IIIa location

Fractio

-1

Fraction

-1

(%)

(cm )

n (%)

(cm )

(%)

PLA

1776.2 ± 1.2

7.8

1754.9 ± 1.1

75.8

1734.2 ± 1.2

16.4

PLA/CNS

1776.5 ± 1.1

6.2

1755.7 ± 1.0

63.8

1734.5 ± 1.0

30.0

PLA/CNC

1776.1 ± 1.3

5.8

1755.5 ± 1.2

66.4

1735.0 ± 1.1

27.8

PLA/CNF

1775.4 ± 1.2

9.2

1755.0 ± 1.1

67.4

1734.2 ± 1.0

23.4

1

a

2

3.7 Crystal structure of nanocomposites

Peak I: free C=O; Peak II: C=O in amorphous regions; Peak III: C=O in crystal regions

3

Figure 8 shows the XRD diffraction spectra of pure PLA and its nanocomposites. The

4

typical PLA diffraction peak at 16.5o was ascribed to amorphous peak37. For all

5

nanocomposites, some new weak diffraction peaks were ascribed to characteristic peaks of

6

nanocelluloses, indicating that the NCs had been successfully loaded into PLA matrix. The

7

CNC and CNF showed three diffraction patterns at 2θ =14.6o, 16.2o, and 22.5o, which were

8

ascribed to typical cellulose I structure. While the diffraction patterns of CNS exhibit the

9

typical cellulose II structure at 2θ = 12.2o, 20.2o, and 21.8o. With the incorporation of

10

nanocelluloses into PLA matrix, the location of peak at 16.5o was not changed, suggesting

11

that the addition of NCs had not changed the crystal structure of nanocomposites. Further,

12

compared to PLA, the full width at half maximum (FWHM)

13

became larger and PLA/CNS showed the largest FWHM value, demonstrating smaller PLA

14

crystals among the nanocomposites.


2θ = 16.5

o

of nanocomposites

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1 2 3

Figure 8. XRD patterns of pure PLA and its nanocomposites. 3.8 Non-isothermal crystallization behavior and crystallization kinetics

4

Figure 9 shows non-isothermal cold-crystallization DSC curve corresponding to second

5

heating scan at different heating rates (β) for PLA nanocomposites. The second heating DSC

6

curve showed some peak of glass transition temperature (Tg), cold-crystallization temperature

7

(Tcc), and melting temperature (Tm), suggesting the first cooling scan could not make the PLA

8

completely crystallized. With the increase of the heating rate, Tcc gradually shifted to the

9

higher temperature, due to the thermal activation. When the heating rate exceeded 10 oC/min,

10

the exothermic and endothermic peaks were significantly weakened so that they were hardly

11

observed. At the slow heating rate, amorphous PLA chains (Figure 9a and b) had enough

12

time to self-regulate, leading to the strengthened cold-crystallization peak along with the

13

multiple-melting behavior. The presence of multiple-melting peaks in PLA nanocomposites

14

may be related to the formation of different crystal structure or stratified population with

15

different perfection degree (or crystallinities)38. This indicates that the incorporation of

16

different NCs into PLA matrix was helpful to form perfect PLA crystals, leading to improved

17

crystallinity in the nanocomposites.



1

2 3

Figure 9. Second heating scanning DSC curves of PLA and nanocomposites at different

4

heating rate: 2 oC/min (a), 5 oC/min (b), 10 oC/min (c) and 20 oC/min (d).

5

Table S3 showed the thermal parameters for PLA and PLA nanocomposites at different

6

heating rates. It can be seen that the crystallinity of pure PLA measured at low heating rate

7

was 3 %, and it was almost determined on heating rate. When the heating rate was greater

8

than 10 oC min-1, the significant decrease of PLA crystallinity was ascribed to the fast loss of

9

crystallization ability. At the low heating rate, the crystallinity of the nanocomposites was

10

increased, this effect was more significant in PLA/CNS or PLA/CNC nanocomposites, and

11

PLA/CNS systems showed the highest crystallinity at all studied heating rates, highlighting

12

the spherical morphology and positive effect of CNS in PLA crystallization. The difference of

13

dispersion state for CNS, CNC, and CNF in PLA matrix was the important factor to influence

14

the different crystallization behavior of binary system39,40. Compared to pure PLA, the

15

cold-crystallization (Tcc) of the nanocomposite with CNS decreased greatly at all heating rates,

16

whereas the incorporation of CNC and CNF would lead to slight reductions of Tcc.


Page 20 of 51

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1

In order to understand the effect of incorporation of different NCs on crystallization rate

2

of PLA, the non-isothermal crystallization kinetic of pure PLA and its nanocomposites was

3

studied. Obviously, when NCs were introduced, the exothermic peak of nanocomposites

4

shifted to low temperature (Table S3). Besides, the presence of NCs could increase the

5

crystallinity, and thus they can act as heterogeneous nucleation agents to form more PLA

6

crystals and ordered crystalline layers. The increase of crystallinity could also contribute to

7

the higher melting point in nanocomposites, which was more obvious at lower heating rates.

8

Furthermore, Avrami equation was used to evaluate the effect of NCs on nucleation and

9

crystallization rate of PLA, because this equation was effective to describe initial nucleation

10

and crystallization process39,41. Avrami equation can be used to describe the non-isothermal

11

crystallization: t

12

log[ ln(1  X t )]  log k  n log t , X t 

 (dH / dt )dt  (dH / dt )dt 0 

(3)

0

13

t1/2  (

ln 2 1/ n ) k

(4)

14

where Xt, 0 and ∞ represented relative crystallinity at crystallization time t, time t =0 and time

15

t=∞, respectively; n was Avrami exponent related to the nucleation mechanism and crystal

16

growth dimension; k was the overall kinetic constant determined by the geometric shape of

17

growth crystal phase; The curves of log[-ln(1- Xt)] versus t was plotted, and n and k were

18

calculated from the slope and intercept of the linear fitting; t1/2 was half-crystallization time

19

defined as the time of crystallization extent up to 50 %41.

20

Figure 10 shows typical relative crystallinity curve and relative Avrami plot (Equation 3)

21

of PLA and PLA nanocomposites, and corresponding results obtained from Avrami plot are

22

listed in Table S4. Obviously, k decreased with the increase of heating rate, while t1/2 value

23

decreased with the heating rate. Generally, the higher k value and lower t1/2 value hinted the



1

faster crystallization rate. In Figure 10a-d, the t1/2 values of PLA/CNS and PLA/CNC binary

2

nanocomposites were lower than that of PLA at all heating rate, while PLA/CNF showed

3

similar half-crystallization time value as that of PLA, suggesting that the crystallization rate

4

of PLA became fast by the corporation of well-dispersed CNS and CNC, while CNF induced

5

almost same crystallization rate of PLA. In addition, Figure 10e-h show the curves of

6

log[-ln(1- Xt)] with a good linear fitted degree, indicating that this Avrami theory was suitable

7

to explain the non-isothermal crystallization of PLA/NC nanocomposite system. When the

8

heating rate was below 10 oC min-1, PLA had the n value of 1.70 ± 0.04-3.23 ± 0.11. This

9

value was consistent with reported literatures26,41, suggesting that the initial nucleation of PLA

10

crystals was spherulite growth and then sheet growth with time. The n value of 1.70 ± 0.04

11

suggested that the pristine crystallization stage of PLA might be nucleation growth of

12

two-dimension, acicular and controlled diffusion at 20 oC min-1 39,40. With the addition of NC,

13

n decreased especially when adding the CNS and CNC. This suggests that CNS and CNC

14

could act as the efficient nucleation agents, further proving the results observed on the DSC

15

curves (Figure 9). Indeed, PLA/CNS and PLA/CNC needed the fewer half-crystallization

16

time than those of PLA/CNF and PLA(Figure 10i). Moreover, at higher rates, n value

17

decreased with addition of NC, indicating that the growth pattern of PLA crystals had been

18

changed. The influence of heating rate on n value might was observed during the changing in

19

growth dimension of PLA crystals and crystallization types.


Page 22 of 51

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1

2

3

4



Page 24 of 51

1 2

Figure 10. Curves and Avrami plotting of relative crystallinity vs. time for PLA (a, e),

3

PLA/CNS (b, f), PLA/CNC (c, g), PLA/CNF (d, h) and the curves at 2 oC min-1 (i).

4

Figure 11a summarizes the influence of β on cold-crystallization temperature (Tcc) of

5

PLA/CNS nanocomposite (as a model sample). Tcc increased with the increase of β, because

6

increased β made the PLA crystalline chains less time to crystallize, and prevented the overall

7

energy release of structure slack. Indeed, glass transition of PLA chains needed to experience

8

the structure slack, where they could change the metastable into a stable state during the

9

crystallization process. Due to above reason, Tcc shifted to higher and wider temperature range.

10

Cold-crystallization active energy (Ecc) was calculated using common Kissinger equation42:

ln( 11

 2

T cc

)

Ecc  const RTcc

(5)

12

where R was the universal gas constant, Tcc was the crystallization peak temperature. Ecc value

13

was obtained from the slope of ln(β/Tcc2) versus 1/Tcc plot. Active energy needed to transfer

14

the molecule fragments to crystallization surface. The smaller the Ecc value was, the easier the

15

polymer crystallization was42. According to the Kissinger equation, the Ecc value of PLA/CNS

16

was about (119.8 ± 1.2 kJ/mol) was smaller than those of pure PLA, PLA/CNC, and

17

PLA/CNF. These results suggest that the CNS exhibited strongest nucleation effect on the

18

PLA crystallization, as compared to CNC and CNF.


Page 25 of 51 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1 2

Figure 11. Plotting of Tcc and kissinger’s as functions of βs for PLA/CNS nanocomposite (a),

3

cold crystallization active energy of pure PLA and PLA nanocomposites (b).

4

3.10 Isothermal crystallization morphology

5

The polarized optical microscopy experiment was utilized to measure the spherulite

6

growth rate of pure PLA, PLA/CNS, PLA/CNC, and PLA/CNF at the isothermal

7

crystallization temperature of 110 oC. As shown in Figure 12, the dimension of PLA

8

spherulite (diameter of 60 ± 2.8 μm) was large, due to the low nucleation density of PLA

9

(Figure 12a). With the addition of CNS, the spherulite dimension in PLA/CNS significantly

10

became smaller (diameter of 33 ± 1.5 μm, Figure 12b), meanwhile the nucleation density was

11

far higher than that of PLA. The radial growth of numerous spherulites of PLA based on CNS

12

as the core was observed in nanocomposites, which was considered as the restricted

13

crystallization produced by CNS nanofillers43. Further, the spherulite dimensions of

14

PLA/CNC and PLA/CNF were 42 ± 2.0 and 56 ± 2.6 μm, respectively (Figure 12c and d). It

15

demonstrates that PLA/CNS had the highest nucleation and spherulite density among all the

16

nanocomposites. These results suggest that CNS and CNC decreased the spherulite dimension

17

through the noncovalent interaction of nanocomposite, promoting the overall crystallization

18

rate and providing the heterogeneous nucleation site for PLA crystallization, due to the

19

heterogeneous nucleation effect and restricted crystalline PLA chains by incorporation of

20

CNS or CNC. However, due to large aspect ratio, the nanofiber entanglements of CNF



1

restricted the conformational and structural transformation of PLA chains, leading to formed

2

imperfect large spherulites, and restricted PLA crystals in the nanocomposite, which was

3

accorded with DSC and XRD results. Moreover, insert Figure 12 illustrates the growth curves

4

of spherulite radius with time during isothermal crystallization process, and the growth rates

5

(G) of spherulites were about 0.35 μm/s, 0.24 μm/s, 0.27 μm/s, and 0.33 μm/s for PLA,

6

PLA/CNS, PLA/CNC, and PLA/CNF, respectively. The radial growth rate of PLA spherulites

7

was the highest and growth rate of spherulites increased with the increase of added NC aspect

8

ratio. To conclude, different NCs enhanced the nucleation abilities of crystal nucleus, while

9

decreased the spherulite growth rate due to the diffusion limitation of molecular chain

10

segments via hydrogen bonds between two phases. Altogether, the rate of induced nucleation

11

crystallization was higher than limitation rate of spherulite growth, and thus the overall

12

crystallization rate and crystallinity of PLA nanocomposites were enhanced.

13

14


Page 26 of 51

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1

Figure 12. Optical microscopic images of spherulite morphology for pure PLA (a), PLA/CNS

2

(b), PLA/CNC (c), and PLA/CNF (d). Inserts are their corresponding radial growth rates of

3

spherulites.

4

3.11 Mechanical property and model analysis

5

The mechanical properties of pure PLA and PLA nanocomposites were measured on

6

mechanical testing equipment (Instron 2345) at 20 oC with a relative humidity (RH) of 65 %

7

crosshead speed of 5 mm min-1. The tensile strength (σ), elongation at break (ε), and Young’s

8

modulus (E) of pure PLA, PLA/CNS, PLA/CNC, and PLA/CNF have been shown in Figure

9

13a and b. The σ and E values for pure PLA were about 21 MPa and 0.9 GPa, respectively. It

10

was found that by incorporation of NCs, both σ and E values of the nanocomposites were

11

gradually improved. Compared with pure PLA, both σ and E values for PLA/CNS

12

nanocomposites were increased by 130 % and 140 %, respectively. This result could be

13

attributed to increased crystallinity and hydrogen bonding interaction between CNS and PLA

14

matrix. In addition, the incorporation of CNC and CNF induced great improvements in the

15

mechanical property of PLA, especially in PLA/CNF. Compared to pure PLA, the σ, and E of

16

PLA/CNC were increased by 210 % and 250 %, by 260 % and 350 % for PLA/CNF. These

17

results can be explained based on good dispersion between NCs and PLA matrix and thus

18

strong hydrogen bonding interactions between different NC and PLA matrix, leading to

19

increase the rigidity of nanocomposites and improvements of mechanical properties. In

20

addition, the large aspect ratio of CNF and their entanglements or inter-bridging could assist

21

in nanofiller-matrix and nanofiller-nanofiller load transfers. Also, the dispersion uniformity of

22

nanofillers and their interfacial adhesion strength with PLA matrix also contributed to the

23

mechanical strength of PLA nanocomposites13,15,26. From above, the nanocomposite with the

24

larger aspect ratio of CNF showed the strongest reinforcing effect on the mechanical strength

25

of PLA, as compared to other NCs with low aspect ratio. It should be noted that compared to



Page 28 of 51

1

pure PLA, the elongation at break of all the nanocomposites was reduced due to the efficient

2

stress transferred from PLA to NCs producing from the uniform dispersion and high

3

alignment of NCs in the PLA matrix.

4

Halpin-Kardos and Ouali models were used to fit the modulus of nanocomposite for

5

evaluating contributions of the NC morphologies and its dispersions on the mechanical

6

strength of PLA nanocomposites. Because Halpin-Kardos and Ouali models were

7

semi-empirical models for short fiber-reinforced nanocomposites with/without percolating

8

nanofiller network and interfacial interaction20,44,45:

9

Ep  Em

1   p f 1   p f

Ef E , p  m Ef Em

Ev  Em 10

1

(6) 

1  2v f

Ef

1  v f

E v  m Ef

,

Em 11

1

(7) 2

EC = 0.85Ep + 0.15Ev

(8)

12 13

where Ep and Ev were the longitudinal and transverse modulus of nanocomposites,

14

respectively. EC was the modulus of 3D randomly oriented nanocomposite based on laminate

15

theory. φf was NC content, Em was Young’s modulus of PLA, Ef was Young’s modulus of

16

NCs. ζ was the shape coefficient depending on NC geometry and orientation. Different

17

equations were proposed to calculate the ζ. Equation ζ = 2L/w was used for relative short NC,

18

such as CNS and CNC42. Equation ζ = (0.5L/w)1.8 was used for CNF with high aspect ratio45.

19

The model equation Ouali model based on percolation theory was as follows45,46:

EC  20

(1  2 f ) Em E f  (1   f ) E f 2 (1   f ) E f  ( f  ) Em


(9)

Page 29 of 51 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1


Ψ=0

 =f ( 2 3

φf ≤ φc

(10)

f  c b ) 1  c φ ＞ φ f c

(11)

φc = 0.7/(L/w)

(12)

4

Where subscript f and m were NC and PLA phase, φf was NC content, E was the modulus. Ψ

5

expressed the percentage of percolation NC network. b was the critical percolation exponent

6

and value of 0.4 was used in three dimension network47. φc was the critical percolation

7

threshold (volume fraction)31. L and w were length and width of NC, respectively. φc of CNS,

8

CNC, and CNF were all 10 %. The modulus (Em) of PLA was 0.96 GPa based on tensile

9

results. The modulus of NC would be changed (50-200 GPa) according to the test methods,

10

cellulose type, and aspect ratio48. The module of CNC, bacterial cellulose and CNFs were

11

about 145, 78-84 and 33 GPa by using atomic force microscope and Raman spectra49-51. In

12

this work, a value of 145 GPa for CNS and CNC, while 80 GPa for CNFs were used for

13

theoretical modeling.

14

Figure 13c compared the experimental modulus of PLA/CNS, PLA/CNC, and PLA/CNF

15

with predicted results based on Halpin-Kardos and Ouali models. At the same NC contents,

16

the predicted values of Halpin-Kardos and Ouali models were both lower than the experiment

17

modulus of PLA/CNS, which may be due to that the reinforcement effect of CNS on PLA

18

mainly originated from the strong interaction between CNS and PLA and the increased

19

crystallinity of nanocomposite. While both two models did not consider these two factors,

20

leading to the theoretically predicted values were on the low side51. The predictions given by

21

Halpin-Kardos and Ouali models were both close to the modulus of PLA/CNC. For PLA/CNF

22

nanocomposite, two models both predicted the modulus with reasonable accuracy. It seems

23

that percolation model could catch the modulus change after the CNF content surpass φc..

24

However, the theoretical φc depended on L and w of CNF obtained from the experiment values.



1

This may be ascribed to two main errors. Firstly, the value of L and w were measured based

2

on the dispersion of CNF in water. CNF might have more agglomeration in PLA matrix than

3

water, due to the high viscosity of PLA solution and the weaker hydrogen bonding interaction

4

with CNFs. Secondly, the dispersion of CNF within PLA matrix was absolutely improved by

5

using ultrasonic mixing and magnetic stirring, which could also change the sizes or actual

6

aspect ratio (lower than 100) of CNFs. These two errors would cause a decrease in aspect

7

ratio of CNF, and thus increase the φc. Thus the NC content was selected at 10 wt% was

8

suitable for precisely comparing and analyzing these two models for PLA/NC

9

nanocomposites.

10

It is well known that both Halpin-Kardos and Ouali models assumed perfect interactions

11

between nanofiller-matrix. Halpin-Kardos model was focused on single nanofiber wrapped in

12

a cylindrical shell of a polymer matrix with negligible filler-filler interactions, while Ouali

13

model took filler-filler interaction and percolation filler phase into Takayanagi model for

14

study the modulus of multiphase polymer system when the nanofiller content exceeded its

15

percolation threshold20,47. Our results showed that Halpin-Kardos model was more accurate

16

for PLA/CNS nanocomposites with no filler-filler interaction (i. e. filler content lower than

17

percolation threshold). Above the filler percolation threshold, the modulus change cannot be

18

captured in the long fiber nanocomposites (PLA/CNF), due to CNF- CNF interaction was not

19

considered in this model. Oppositely, Ouali model predicted the trend of PLA/CNF modulus

20

and provided higher prediction accuracy than Halpin-Kardos model. The difference of

21

prediction results highlighted the importance of filler-filler interaction on performances of

22

nanocomposites with high nanofiller loadings.

23

In Figure 13c, model predictions and experimental values were shown at various NC

24

contents, CNF could obtain higher nanocomposite modulus than CNS and CNC. This was

25

mainly due to the larger aspect ratio of CNF proved by SEM observations and model


Page 30 of 51

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1

equations. Indeed, numerous CNF fiber entanglements and percolation network adhered by

2

hydrogen bonds and mechanical interlocking were beneficial to achieve high modulus of PLA

3

nanocomposites (discussed below in Figure 14 and Figure 15). These results showed great

4

potential applications to develop new biopolymer nanocomposites. The high strength of short

5

CNC or CNS was not often used, due to their aspect ratio was too small to transfer all stress

6

from matrix to nanofiber52. As a result, the long nanofiber with relatively low strength and

7

modulus was better than the short high strength nanocrystals and nanospheres in

8

nanofillers-reinforced nanocomposites. Long nanofiber would reach the percolation at low

9

loading levels. The fiber-fiber interaction or percolation network was beneficial to further

10

improve the mechanical property of nanocomposites20. Therefore, this study provides a

11

method to find suitable NCs with different aspect ratios as reinforcement in PLA

12

nanocomposites according to economic benefits, fast crystallization rate, and high strength.

13

14



1

Figure 13. Mechanical properties (a, b) of pure PLA and nanocomposites, and Young’s

2

modulus as a function of NC content: experimental value vs. model predictions in

3

Halpin-Kardos model and Ouali model (c).

4

Tensile samples of pure PLA and its nanocomposites with increasing strain during the

5

tests under three states (before tensile test, during deformation, after tensile test) were

6

observed by using SEM. As shown in Figure 14 and Figure S2, the fractured surface of pure

7

PLA showed typical brittle deformation (relatively regular surfaces, Figure 14a), while the

8

nanocomposites gave plastic deformation (rough and irregular surfaces, voids and pull-out

9

fibrils). Figure 14b and Figure S2 (b, c) clearly showed the good dispersion of CNS as white

10

dots within PLA nanocomposite film without obvious agglomerations after tensile tests

11

(deformation). Figure 14c and d (Figure S2 (d-i)) proved that the diameter of nanofibrils was

12

less than 50 nm. These fibrils played a bridging role in the crazes produced in samples,

13

benefitting from the increases in tensile strength and Young’s modulus. In this stage, the

14

component of these nanofibrils was not clear. This may be due CNC (or CNF) fibers (or

15

bundles) wrapped by PLA. Through the SEM observation, PLA/CNF sample showed more

16

nanofibrils than PLA/CNC, which meight be attributed to the fiber inter-entanglements and

17

larger aspect ratio of CNF (Figure 14d and Figure S2j). Figure 15 illustrates the fractured

18

mechanism of PLA reinforced with CNS, CNC, and CNF. Due to the larger length and higher

19

flexibility of CNF, CNF could bridge crazes at multiple places53. The longer length, network

20

structure, and entanglements of the connecting fibers promoted a large number of visible

21

pull-out nanofibers and interlocked fibers at PLA/CNF fractured surfaces (insert Figure 14d`).

22

Moreover, the results of UV-vis spectra confirmed that all the nanocomposite films could

23

obtain visibly good transparency (Figure S3), but PLA/CNF nanocomposite exhibited lower

24

transmittance than PLA/CNS and PLA/CNC nanocomposites, possibly due to the larger

25

aspect ratio of CNF and interlocking within the PLA matrix.


Page 32 of 51

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1

2 3

Figure 14. Fractured surface of pure PLA (a), PLA/CNS (b), PLA/CNC (c), and PLA/CNF (d)

4

nanocomposite films after tensile strength (insert Figure 14d’ is the interlocking between

5

fibrils at the fractured surface).

6 7

Figure 15. The fractured mechanism of PLA/CNS, PLA/CNC and PLA/CNF nanocomposite

8

films.



1

Page 34 of 51

3.12 Thermal stability

2

One of the main purposes of adding NC is to enhance the thermal stability of PLA

3

bio-packaging to avoid the risk of thermal degradation because melt-processing of PLA films

4

needs to be treated in the high-temperature region of 150-180

5

thermogravimetric analysis (TGA) is used to study the influence of NCs on PLA thermal

6

degradation property, and Table 4 summarizes the main thermal degradation parameters in

7

this study: initial degradation temperature (T0), temperature at 5% weight loss (T5%),

8

maximum degradation temperature (Tmax), and complete decomposition temperature (Tf) and

9

the thermal processing windows (Tmax-Tm1, Tm1 is the melting temperature at a heating rate of

10

10 oC min-1) of nanocomposites. Figure (16a and b) show the TGA and DTG curves of PLA

11

and nanocomposites with various NCs. The thermal degradation of pure PLA and

12

nanocomposites were produced by one-step degradation process with maximum degradation

13

temperature. Compared with PLA, the thermal degradation curves with one- step degradation

14

process shifted to a higher temperature for the PLA/CNS, PLA/CNC, and PLA/CNF

15

nanocomposites. It indicates that the thermal stability of PLA based nanocomposites was

16

improved (Table 4), which was consistent with PLA materials reinforced with other NCs56.

17

As shown in Figure 16b, the PLA/CNF exhibited lower thermal stability (degradation

18

temperature), compared with PLA/CNS and PLA/CNC. This was ascribed to the lower

19

crystallinity and weak interfacial interaction between PLA and CNF54,55. Furthermore, this

20

result also suggests that the positive effect of CNS and CNC on the interaction between PLA

21

and nanofillers, proving the occurrence of interaction for PLA-nanofillers. Meanwhile, the

22

incorporation of CNC made T0 of PLA nancomposite be higher than PLA, while compared to

23

PLA, T5%, Tmax, and Tf increased by 16.3, 21.5, and 26.1 oC, respectively (Table 4). It should

24

be noted that no degradations occurred in the range from room temperature to 200 oC and

25

wider thermal processing windows (higher Tmax-Tm1 of 217.1-224.7 oC) were found for all the


o 54

C . Therefore,

Page 35 of 51 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

nanocomposites (Table 4), which indicated that the PLA nanocomposites could be

2

melt-processed to packaging films without degradations.

3

The apparent activation energies of pure PLA and nanocomposites were obtained from TGA

4

data by using Horowitz and Metzger equation57:

5

ln[ln(

W0 E )]  a 2 WT RTs

(13)

6

Where W0 was initial weight of the polymer; WT was the residual weight of polymer at

7

temperature T; Ts was the temperature measured at 36.79 % weight loss; θ was T - Ts; R was

8

the gas constant.

9

Figure 16d shows the plotting of In[In(W0/WT)] vs. θ of main degradation stage of pure

10

PLA and nanocomposite. Table 4 lists the average apparent active energy (Ea) obtained from

11

the slope. The higher the Ea value was, the faster the degradation rate was38. The Ea results in

12

Table 4 illustrated the Ea value of the nanocomposite was larger than that pure PLA, meaning

13

that the onset degradation temperature of nanocomposites was high, thus leading to the

14

degradation rate would be faster than that of pure PLA. During the degradation of

15

nanocomposites, the introduction of NCs improved thermal degradation temperature of PLA,

16

and thus the thermal degradation of nanocomposites begun to occur at a higher temperature

17

than pure PLA. The higher temperature was the main factor to cause an increased degradation

18

rate of the nanocomposites. Similar explanations for degradation rates were also found for NC

19

and other nanofiller based nanocomposites26,34,38.



Page 36 of 51

1

2 3

Figure 16. TGA (a), DTG (b) curves, thermal parameters (c) and plotting of ln[ln(W0/WT)] vs.

4

θ (d) of pure PLA and its nanocomposites.

5

Table 4. Thermal analysis parameters of pure PLA and its nanocomposites. Sample

T0 (oC)

T5% (oC)

Tmax(oC)

PLA

335.7

325.9

354.6

± 3.6

± 3.5

± 3.7

353.1

342.2

376.1

PLA/CNS ± 3.7

± 3.7

± 3.8

352.3

331.7

377.0

PLA/CNC

PLA/CNF

± 3.7

± 3.6

± 3.8

342.9

307.2

370.9

± 3.7

± 3.2

± 3.7

Tf (oC)

Tmax-Tm1 (oC)

363.9 ± 3.6

205.9 ± 1.1

390.0 ± 3.8

222.0 ± 1.2

389.8 ± 3.9

224.7 ± 1.2

381.6 ± 3.8

217.1 ± 1.1


Ea (kJ/mol)

319.95 ± 2.68

414.77 ± 3.12

456.09 ± 3.31

409.82 ± 2.97

Page 37 of 51 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1


3.12 Water uptake, WVP, and migration property

2

Figure 17a shows the water uptake of pure PLA and nanocomposites immersed in water.

3

Clearly, the water uptake of pure PLA was high, about up to 6.50 ± 0.08 % after immersing

4

for long enough time. The water uptake of PLA/CNS significantly decreased to 1.42 ± 0.02 %

5

(Figure 17a), which was caused by the strong filler-matrix interfacial interaction and the

6

increased crystallinity. In Figure 17a, the water uptake of PLA/CNC nanocomposite was

7

about 1.05 ± 0.01 %, and decreased by 83.3 %, compared to pure PLA. The rod-like CNC

8

induced an increase in PLA tortuosity, leading to lower water uptake than that of PLA/CNS.

9

Besides, compared to above two kinds of nanocomposites, the water uptake of PLA/CNF was

10

slightly increased, probably due to voids inside nanocomposite film caused by more CNF

11

entanglements and weak CNF-matrix interaction. Indeed, PLA/CNF showed the relatively

12

larger porosity of 0.8% than 0.1-0.5% for PLA and other nanocomposites (Supplementary

13

Information).

14

Figure 17a also shows the WVP of pure PLA and nanocomposites. The WVP of pure

15

PLA was 2.15 ± 0.07×10-14 Kg m m-2 s-1 Pa-1. However, after incorporating with NC, the

16

WVP values were reduced by 76.3 %, 79.1 %, and 66.5 % for PLA/CNS, PLA/CNC and

17

PLA/CNF nanocomposite film, respectively. It is obvious that the WVP value of PLA/CNF

18

film was slightly higher than those of two former nanocomposite films. This result was due to

19

the differences in shape and structure of nanofillers, i. e. caused by the spherical shape of

20

CNS, the rod-like shape of CNC, and nanofibrous shape of CNF. The good dispersion of CNS

21

and CNC in PLA matrix may form stronger interfacial interaction and more efficient zigzag

22

paths than CNF. The similar effect of nanofillers on WVP of the biopolymer nanocomposite

23

film was also reported58.

24

Figure 17b shows the overall migration results of pure PLA and its nanocomposites in

25

two liquid foods simulates (isooctane and 10 % (v/v) ethanol). All the samples were far below



1

the specified overall migration limits for food contact material (60 mg kg-1) (Commission

2

Regulation EU 10/2011)26,34. It is interesting to mention that the migration of pure PLA in

3

ethanol 10 % (v/v) was higher than that in isooctane, which was due to the hydrolysis and

4

formation of low molecule oligomers for PLA rather than migration59. However, except

5

PLA/CNF, other nanocomposites showed higher overall migration levels in isooctane. This

6

result was due to the increase of polymer swelling caused by isooctane through increasing the

7

free volumes into a polymer matrix to decrease the resistance on the release of additives into

8

medium60, while higher migration level in ethanol of PLA/CNF was also higher than that in

9

isooctane, because of the entanglements of nanofibers restricted swelling of the biopolymer.

10

Compared to pure PLA, the overall migration levels of PLA/CNS and PLA/CNC were

11

decreased by 83% and 85%, due to restricted migration of simulates into PLA matrix through

12

the increased interfacial interaction between CNS or CNC and PLA. The migration level of

13

PLA/CNF was slightly increased, probably due to weakened filler-matrix interaction caused

14

by the slight agglomeration of CNFs.

15

More importantly, compared to Tmax, andWVP values of other PLA-based

16

nanocomposite systems (PLA/CNTof 61, PLA/CNC (Tmax of 4.4 oC, of

17

WVP of 53 %)62, PLA/tGO (Tmax of 32.1 oC, of )63 and PLA/clay (Tmax of

18

-4.3 oC, of WVP of 36.4 %)64), our PLA/NCs nanocomposite films had superior

19

mechanical properties (of 130-260 %), thermal resistance (Tmax of 16.3-22.4 oC) and

20

water vapor barrier performance (WVP of 66.5-79.1 %), which were more helpful for

21

high-barrier food packaging to compete for commercial food packaging films in the market

22

(Figure 18).


Page 38 of 51

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1 2

Figure 17. Water uptake, WVP (a) and migration levels (b) of pure PLA and nanocomposites.

3 4

Figure 18. Overview of Tmax (increase value of Tmax), ( increase the percentage of tensile

5

strength)andWVP (decrease percentage of WVP) vs other work. The Tmax, and WVP

6

of PLA/CNS(a), PLA/CNC(b), PLA/CNF (c) in this work; Tmax and of PLA/CNT61(d),

7

PLA/CNC62(e), PLA/tGO63(f), PLA/clay64 (g), WVP of PLA/CNC65 (h) and PLA/clay66 (i).

8 9

4. CONCLUSIONS

10

Nanocelluloses (NCs) with different aspect ratios were prepared by HCOOH/HCl

11

hydrolysis of lyocell fibers, MCC and ginger fibers, which were used to reinforce PLA via

12

simple solution casting. The NC surface was chemically modified by Fischer esterification

13

with hydrophobic formate groups to improve the NC dispersion within PLA matrix.



1

Systematical comparison in the effect of CNS, CNC, and CNF as reinforcing agents on

2

microstructures, interfacial bonds with PLA, crystallization kinetics, thermal, mechanical and

3

barrier properties of the resulting nanocomposites were investigated. With the incorporation

4

of NCs, the crystallization ability and mechanical strength of PLA nanocomposites were

5

improved greatly. Especially, the PLA/CNS showed the highest crystallinity (19.8 ± 0.4 %)

6

with smaller spherulite size (33 ± 1.5 µm), while compared to PLA, the PLA/CNF

7

nanocomposites exhibited the largest tensile strength and Young’s modulus, which were

8

increased by 260 % and 350 %, respectively. It indicates that because of high specific surface

9

area, CNS could induce stronger heterogeneous nucleation effect on the PLA crystallization

10

than CNC and CNF, whereas the PLA/CNF showed the strongest reinforcing effect on the

11

mechanical strength of PLA nanocomposite films, due to the much larger aspect ratio of CNF

12

and their interlocking or percolation network caused by filler-matrix interfacial bonds.

13

Moreover, through simulating and comparing moduli of the nanocomposites, Ouali model

14

was more accurate for PLA/CNF, while Halpin-Kardos model was more suitable for

15

PLA/CNS and PLA/CNC nanocomposites. Furthermore, all the PLA/NC nanocomposite

16

films exhibited obvious reductions in the water uptake, WVP and overall migration levels in

17

two liquid food simulates below the specified overall migration restriction (60 mg kg-1), and

18

the PLA/CNC nanocomposite films showed the best barrier property and lowest migration

19

levels than CNS and CNF based on the factors of hydrogen bonding interaction, the

20

crystallinity, and interfacial tortuosity. From above, this comparative study was very helpful to

21

reasonably choose of nanocelluloses as nucleation/reinforcing agents in biopolymer

22

nanocomposites for robust-barrier packagings.

23 24



25

*Supporting Information

ASSOCIATED CONTENT


Page 40 of 51

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1

The characterization parts of PLA and its nanocomposites. AFM images of pure PLA and

2

nanocomposites (Figure S1), SEM images for fractured surface of pure PLA (before tensile

3

test) (a), pure PLA (after tensile test) (b), PLA/CNS (before tensile test) (c), PLA/CNS (after

4

tensile test) (d), PLA/CNC (before tensile test) (e), PLA/CNC during deformation (f),

5

PLA/CNC (after tensile test) (g). PLA/CNF (before tensile test) (h), PLA/CNF during

6

deformation (i) and PLA/CNF (after tensile test) (j) (Figure S2), the transparence test of pure

7

PLA and nanocomposites (insert are the visual observations of the nanocomposite films)

8

(Figure S3); The wavenumbers and vibration types of cellulose materials and PLA

9

nanocomposites (Table S1), The 2θ and planes of cellulose materials and nanocelluloses

10

(Table S2), Thermal properties of PLA and its nanocomposites at the second heating scan

11

(Table S3) and Avrami kinetic parameters of non-isothermal crystallization for PLA and its

12

nanocomposites (Table S4). This material is available free of charge via the Internet at http://

13

pubs.acs.org.

14



15

Corresponding Author

16

* Hou-Yong Yu (H.Y. Yu); Tel.: 86 571 86843618; E–mail addresses: [email protected].

17

Notes

18

The authors declare no competing financial interest.

19



AUTHOR INFORMATION

ACKNOWLEDGMENT

20

The work is funded by the National Natural Science Foundation of China (51403187),

21

Key Program for International S&T Innovation Cooperation Projects of China

22

(2016YFE0131400), the public technology research plan of Zhejiang Province (2017C37014),

23

“521” Talent Project of Zhejiang Sci-Tech University, Candidates of Young and Middle

24

Aged Academic Leader of Zhejiang Province and State Key Laboratory for Modification of

25

Chemical Fibers and Polymer Materials, Donghua University (LK1713).



Page 42 of 51

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

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