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S1 Appendix A. Supporting Information

Antipathogenic upcycling of face mask waste

into separation materials

using green solvents

Joyce Cavalcante, Rifan Hardian, Gyorgy Szekely^*

Advanced Membranes and Porous Materials Center, Physical Science and Engineering Division (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia

*Corresponding author: +966567271203, [email protected], www.szekelygroup.com

Fig. S1. Solvents used in this study. ... S3 Fig. S2. Solutes used in this study. ... S3 Fig. S3. The macromorphology of the recovered polymers... S4 Fig S4. Membranes fabricated with face masks from different commercial brands ... S5 Fig. S5. Derivative weight loss thermograms for face mask layers. ... S5 Fig. S6. DSC of the face mask layers. ... S6 Fig. S7. Crystallinity profile of starting materials, recovered polymers, and membranes... S7 Fig. S8. SEM images of the recovered polymers. ... S8 Fig. S9. Cross-section quantification of thicknesses for membranes ... S8 Fig. S10: Top surface SEM analysis in higher magnification. ... S9 Fig. S11. Schematic of the multistage crossflow nanofiltration apparatus ... S12

List of Tables

Table S1. Green solvent screening and dissolution tests (performed for up to 2 h). ... S3 Table S2. Summarized results of derivative thermogravimetry (DTG) analysis. ... S5 Table S3. Thermal parameters of face mask layers (starting materials). ... S6 Table S4. Determination of interplanar distances (d) within the fabricated membranes ... S6 Table S5. Quantification of crystalline and amorphous phases. ... S7 Table S6. Roughness quantification values for the fabricated membranes, obtained via AFM. ... S9 Table S7. Physical properties of acetonitrile(MeCN) ... S9 Table S8. Rejection values as the functions of molecular weight. ... S12 Table S9. Acetonitrile flux values for M3, M4, and M5 as the functions of applied pressure. ... S13 Table S10. Rejection of API for M3, M4, and M5 in acetonitrile at 30 bar. ... S133 Table S11. Real life projection of the Standard SWMM modules from the described methodology. ... S14

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S3

1. Details and characterization

Fig. S1. Solvents used in this study.

Fig. S2. Solutes used in this study.

Table S1. Green solvent screening and dissolution tests (performed for up to 2 h).

Temperature (℃)

propylene

carbonate rhodiasolv® γ-valerolactone p-cymene o-

xylene p-xylene 20

60 120 140

Face mask sample does not dissolve Face mask sample partially dissolves

Face mask sample completely dissolves

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S4 Fig. S3. (a–h) The macromorphology of the recovered polymers (𝑃 _𝑅𝑇^𝐸 , 𝑃 _𝑅𝑇^𝐸𝑆, 𝑃 _𝑅𝑇^𝐺𝑉, 𝑃 _𝑅𝑇^𝑇 , 𝑃 _𝐵𝑃^𝐸 , 𝑃 _𝐵𝑃^𝐸𝑆, 𝑃 _𝐵𝑃^𝐺𝑉, and 𝑃 𝐵𝑃𝑇 ).

For the analysis of membrane formation from multiple commercial brands, we selected SHUMU Surgical Face Masks (model PR-01 from Shenzhen Yansen Optoelectronics CO. LTD., China), YANNA BABY Disposable Medical Surgical Face Masks (model KF-L KD-KZ2005 from Guandong Kaidi Garments CO. LTD, China), OptiTect Disposable mask (model 3 PLY face masks with loops from Dongtai Junfan Protective Products CO. LTD, China), 3 Health Care Máscara cirúrgica (3M, Brazil), and UNIform Disposable Mask For Children (model 150x95mm, Saudi Arabia). Dope solutions for membrane casting were prepared by dissolving each face mask at a 15 wt% concentration in p-cymene at 140 ℃ over 20 min. The dope solutions were cast on a glass plate at 100 ℃ using an Elcometer 4340 film applicator at a casting speed of 6 cm s⁻¹ and a blade gap of 300 μm. The resulting film was immediately immersed into a 10 L coagulation bath of ethanol at 20°C. The formed free-standing membranes were rolled and stored in ethanol. All masks were processed under the same conditions and resulted in similar membranes as seen in Fig S4.

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S5 Fig S4. Membranes fabricated with face masks from different commercial brands. (a) SHUMU, (b) YANNA BABY, (c) OptiTect, (d) 3 Health Care, and (e) UNIform.

Table S2. Summarized results of derivative thermogravimetry (DTG) analysis, where Tmax represents the temperature at which the maximum weight loss rate occurred.

Starting material DTG Tmax (℃)

FMIL 440.30

FMML 438.69

FMOL 436.02

Fig. S5. Derivative weight loss thermograms for the inner, middle, and outer face mask layers (FMIL, FMML, and FMOL, respectively).

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S6 Fig. S6. DSC of the face mask layers: a) inner, b) middle, and c) outer layer of surgical face mask.

Table S3. Thermal parameters of face mask layers (starting materials).

Starting material

Tg (℃) Tc (℃) Enthalpy of Crystallization (ΔHc) (J.g^–1)

Tm (℃) Enthalpy of Melting (ΔHm) (J.g^–1)

FMIL 0.52 119.64 102.32 162.65 86.46

FMML 0.50 121.95 103.21 160.58 84.78

FMOL 0.52 131.76 100.10 163.08 83.54

Table S4. Determination of interplanar distances (d) within the fabricated membranes according to Bragg’s law (nλ = 2d sin(θ)) for a copper K-α wavelength (λ) of 1.5406 Å.

2θ (°) Interplanar distance (Å)

21.90 4.13

21.46 4.25

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S7 Fig. S7. Crystallinity profile of starting materials (FMIL, FMML, and FMOL), recovered polymers, and membranes M1, M2, M3, M4, and M5.

Table S5. Quantification of crystalline and amorphous phases of starting materials (FMIL, FMML, and FMOL), recovered polymers, and membranes M1, M2, M3, M4, and M5.

Crystalline Amorphous Standard deviation

FMIL 48.1 51.9 4.5

FMML 49.5 50.5 5.6

FMOL 46.9 53.1 3.2

𝑷 _𝑹𝑻^𝑬 63.6 36.4 2.7

𝑷 _𝑹𝑻^𝑬𝑺 62.0 38.0 2.6

𝑷 _𝑹𝑻^𝑮𝑽 55.5 44.5 3.5

𝑷 _𝑹𝑻^𝑻 62.2 37.8 3.0

𝑷 _𝑩𝑷^𝑬 67.5 32.5 1.9

𝑷 _𝑩𝑷^𝑬𝑺 65.4 34.6 2.0

𝑷 _𝑩𝑷^𝑮𝑽 61.1 38.9 2.7

𝑷 _𝑩𝑷^𝑻 67.3 32.7 2.5

M1 37.5 62.5 2.0

M2 39.5 60.5 1.9

M3 38.8 61.2 2.6

M4 41.8 58.2 1.7

M5 44.7 55.3 1.7

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S8 Fig. S8. SEM images of the recovered polymers: 𝑃 𝑅𝑇 𝐸 (a and i), 𝑃 𝑅𝑇𝐸𝑆(b and j), 𝑃 𝑅𝑇𝐺𝑉 (c and k), 𝑃 𝑅𝑇𝑇 (d and l), 𝑃 𝐵𝑃𝐸 (e and m), 𝑃 𝐵𝑃𝐸𝑆 (f and n), 𝑃 𝐵𝑃𝐺𝑉 (g and o), and 𝑃 𝐵𝑃𝑇 (h and p).

Fig. S9. Cross-section quantification of thicknesses for membranes a) M1, b) M2, c) M3, d) M4, and e) M5 and the presence of bridging molecules (indicated by arrows) between the PP microspheres in f) M1, g) M2, h) M3, i) M4, and j) M5.

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S9 Fig. S10: Top surface SEM analysis in higher magnification for M1 (a and f), M2 (b and g), M3 (c and h), M4 (d and i), and M5 (e and j). Figs. a–e were obtained at magnification 25k times, and Figs. f–j were obtained at magnification 50k times.

Table S6. Roughness quantification values for the fabricated membranes, obtained via AFM.

Ra (nm) Ra standard

deviation Rq (nm) Rq standard deviation

M1 230.45 32.03 294.4 32.95

M2 269.45 39.38 331.05 54.09

M3 216.90 6.22 275.45 13.78

M4 240.60 45.11 294.75 54.23

M5 119.15 0.77 159.15 1.62

2. Pore-size calculations

Table S7. Physical properties of acetonitrile(MeCN).[1]

Solvent Mwa

(Da) dmb

(nm)

Η^c (mPa s)

Vmd

(cm³ mol⁻¹)

ρ^e (g ml⁻¹)

δdf

(MPa^0.5)

δPg

(MPa^0.5)

δhh

(MPa^0.5)

δti

(MPa^0.5)

MeCN 41.05 0.55 0.343 52.5 0.786 15.3 18 6.1 24.4

a Molar mass; ^b diameter; ^cdynamic viscosity; ^d molar volume; ^edensity; ^f,g,h,i Hansen parameters (dispersion, polar, hydrogen bonding, and total, respectively).

As suggested by Livingston et al.,[2] the permeance of a solvent can be correlated to its physical properties. The diameter of acetonitrile was calculated as follows:

𝑑_m= 2 ∙ (^3𝑉^𝑚

4𝜋𝑁_𝐴)

1

3, S1

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S10 where Vm is the molar volume obtained from the solvent density and NA is Avogadro’s number.

The Hagen–Poiseuille equation defines the volumetric flux (Jv) through a membrane comprising uniform capillaries:

l P J_v_i rⁱ

0 2

,

8 



= 

, S2

where  is the porosity, ΔP is the transmembrane pressure, l is the capillary length, 0 is the solvent bulk viscosity, and ri is the capillary radius. Next, using the pore flow rate (Qp,i), the flow through a pore of radius ri can be calculated as follows:

l P Q_p_i rⁱ

0 4

,

8 





=

. S3

The overall solute rejection can be calculated using the following equation:

(

¹

)(

^exp( ⁾

)

1 1

, ,

,

ij e ij

c ij

ij c ij

ij K P

R K

−



−

− 

=

, S4

where Φij is the partition coefficient and λij is the ratio between the solute radius rs,j (the subindex j indicates the solute) and pore radius ri (the subindex i indicates the pore-size class in the discretization method):

(

1 _ij

)

²

ij = −

 ; S5

i j s

ij r

r_,



=

. S6

Assuming that a steric interaction occurs between the solute and pore walls, the solute convective Kc,ij and diffusive Kd,ij hindrance factors can be expressed as follows:

( ) (

² ³

)

,_ij 2 _ij 1 0.054 _ij 0.988 _ij 0.44 _ij

Kc = − +  −  +  ; S7

3 2

,_ij 1 2.3 _ij 1.154 _ij 0.224 _ij

Kd = −  +  + 

. S8

The Peclet number (Pe,ij) characterizing the pore flow is defined as











=  

i p i j s ij d

ij c ij

e

P r D K P K

, 2

, ,

,

8 

. S9

The diffusivity Ds,ij of a solute of radius rs,j is calculated using the Stokes–Einstein equation:

j s i p ij

s r

D kT

, ,

, ⁼

6 

, S10

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S11 where k is the Boltzmann constant and T is the temperature. The Wilke–Chang formula can be used to solve the above equation and estimate the solute’s diffusivity:

6 . 0

, , 8

,

7 . 4 10

j m i p

solv ij

s V

M D T



−





=

, S11

where Msolv is the molecular weight (Mw) of the solvent molecule,  is a dimensionless solvent parameter, and Vm,j is the solute molar volume (in cm³ g mol⁻¹). If the rejection value R(r) is a continuous function of the pore radius r, then PDF fR(r) is introduced to describe the pore size distribution:

( )



 



− +

= b

b r r b

r r

f 2

) 2 /

* / exp log(

2 ) 1 (

2

 _, S12

where











 



 

 +

=

2

1 *

log r

b 

. S13

To calculate the function f(r), the mean pore radius (r*) and its standard deviation () must be estimated. For simplification, the distribution function was truncated to rmax:

dr r r f

f r f

r R R

R

=



m ax

0 ( )

1 )

( ) ( '

. S14

The overall rejection value for the pore radii of 0 < r < rmax can then be calculated as



=



m ax m ax

0

4 0

4

) ( / ) ( '

) ( / ) ( ) ( '

r R r

R j

dr r r r f

dr r r R r r f

R 



. S15

By implementing the abovementioned models, the mean pore size and its standard deviation can be fitted by minimizing the error.

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S12

3. Membrane performance

Fig. S11. Schematic of the multistage crossflow nanofiltration apparatus used for membrane testing.

Table S8. Rejection values as the functions of molecular weight, obtained for M3, M4, and M5 in acetonitrile at 30 bar.

Rejection Standard deviation

Molecular weight (g mol^–1)

Retentate M3 M4 M5 M3 M4 M5

236 7251 67.50 63.24 59.32 1.59 1.26 0.93

295 8426 72.36 66.67 60.94 1.63 0.79 0.81

395 10464 76.85 71.42 64.96 1.13 1.08 0.89

495 11951 81.44 74.48 69.12 1.65 1.47 0.84

595 11516 85.47 79.30 72.46 1.43 1.32 0.82

695 8487 89.61 84.25 76.99 1.75 1.23 1.53

795 6312 92.36 87.49 81.53 1.66 1.11 1.70

895 5719 94.36 89.83 84.91 1.81 1.12 1.63

995 5141 95.54 92.65 88.00 1.58 1.59 1.73

1095 5275 97.08 95.00 91.23 0.32 1.04 1.35

1195 5471 97.83 96.90 94.07 0.16 0.39 0.69

1300 5115 98.75 97.32 95.35 0.22 0.55 1.04

1400 4301 99.27 98.19 96.33 0.08 0.17 0.24

1500 3151 99.45 98.59 96.87 0.20 0.31 1.14

1600 1958 99.62 98.62 98.28 0.54 0.46 1.50

1700 1412 100.00 98.98 99.13 0.00 0.25 1.23

1800 915 100.00 100.00 99.25 0.00 0.00 1.07

1900 816 100.00 100.00 100.00 0.00 0.00 0.00

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S13 Table S9. Acetonitrile flux values for M3, M4, and M5 as the functions of applied pressure.

Flux (L m^–2 h^–1) Standard deviation Pressure

(bar) M3 M4 M5 M3 M4 M5

10 44.7 53.2 63.1 4.3 2.9 3.6

20 96.7 114.1 129.8 4.8 3.6 4.3

30 143.7 168.1 192.3 5.9 8.9 8.2

Table S10. Rejection of API for M3, M4, and M5 in acetonitrile at 30 bar.

API Abbreviation Molecular Weight (g mol^–1)

Days Rejection (%)

Standard Deviation

Roxithromycin RT 837

1 97.5 1.2

2 97.9 1.0

3 97.9 0.9

4 98.1 0.9

5 98.2 0.7

Rose bengal RB 874

1 99.3 0.9

2 99.4 0.6

3 99.6 0.5

4 99.6 0.6

5 99.5 0.3

4. Scalability considerations

During the COVID-19 pandemic, approx. 61 billion face masks have been used annually in European countries, and Asian countries have consumed approx. 290 billion face masks annually.[3] These numbers, even if a small percentage of the masks are recycled, support the upcycling of face mask waste into a sufficiently large-scale production of membranes modules.

Note that our proposed methodology did not convert each single face mask into a small size

membrane limited by the size of the face mask. In the proposed methodology, the face masks were

accumulated and dissolved simultaneously in a green solvent system to fabricate the OSN

membranes where the size of the membranes can be adjusted based on the amount of the

accumulated face masks. We calculated the real-life impacts of membrane production associated

with the fabrication of standard module sizes for OSN membranes (spiral wound membrane

module, SWMM) and the data has been used for the calculations shown in Table S11. The

projection takes into consideration that the casting method of production can be applied

continuously and, therefore, the simple and scalable methodology described in our manuscript can

easily be adopted by the membrane industry.

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S14 Table S11. Real life projection and quantification of the standard SWMM modules that can be fabricated annually via the described methodology. The standard sizes of the SWMM were obtained from the data reported in the literature.[4]

Number of face masks

Mass (tonne)

Membrane area (km²)

2.5"

SWMM^*

4"

SWMM^**

8"

SWMM^***

Lab scale 2 3 × 10^–6 3 × 10^–8 - - -

Europe's annual consumption

61 billion 9 × 10⁴ 915 457 million

152

million 30 million Asia's annual

consumption

290

billion 4 × 10⁵ 4350 2175 million

725 million

145 million

* each 2.5" SWMM has membrane area of 2 m²

** each 4" SWMM has membrane area of 6 m²

*** each 8" SWMM has membrane area of 30 m²

The annual output capability of companies vary within the range of 1–100 km

²

of nanofiltration membranes. Which means that approx. 10% of the European face mask waste needs to be recycled in order to meet the maximum demand. In Asia this value drops to approx. 2%. To showcase a more specific example: the annual production of Hunan Keensen Technology Co., Ltd.

is 3 km

²

, which corresponds to only 0.06% of the face mask waste production in Asia.[5] This quantification presents a validation that the material selection displayed in our work can fulfill the requirements of the membrane industry by introducing an easy, efficient and sustainable recycling method.

5. References

[1] J. R. Reimers, L. E. Hall, The Solvation of Acetonitrile. J. Am. Chem. Soc. 121 (1999), 3730–

3744. https://doi.org/10.1021/ja983878n.

[2] S. Karan, Z. Jiang, A. G. Livingston, Sub–10 Nm Polyamide Nanofilms with Ultrafast Solvent Transport for Molecular Separation.

Science. 348 (2015) 1347–1351.

https://doi.org/10.1126/science.aaa5058.

[3] Face Mask Market by Product Type, Material Type, Usability and By Region 2020-2028 https://www.emergenresearch.com/industry-report/face-mask-market (accessed 2021-12- 21).

[4] G. Szekely, M. F. Jimenez-Solomon, P. Marchetti, J. F. Kim, A. G. Livingston, Sustainability Assessment of Organic Solvent Nanofiltration: From Fabrication to Application.

Green Chem. 16 (2014) 4440–4473. https://doi.org/10.1039/C4GC00701H.

[5]

Hunan Keensen Technology Co., Ltd. RO and NF Membrane Element, (2015).

https://company.aquatechtrade.com/Hunan-Keensen-Technology-Co---Ltd-

?Language=EN&eventid=15524&account=00507784-0 (accessed 2021-12-21).

Table of Contents

Antipathogenic upcycling of face mask waste into separation materials using green solvents

Publisher Elsevier BV

Download date 2023-12-07 20:23:38

Antipathogenic upcycling of face mask waste

into separation materials

using green solvents