Near infrared spectroscopy as a tool for in-line control of process and material properties of PLA biopolymers

Transcription

1 Near infrared spectroscopy as a tool for in-line control of process and material properties of PLA biopolymers OMC conference Karlsruhe W. Becker Fraunhofer Institute for Chemical Technology Fh-ICT Pfinztal; Germany

2 Content - Introduction - Equipment - Measurements - Results - Conclusion - Future work

3 Introduction - Polylactic acid (PLA) Thermoplastic from natural renewable resources - Substitution for synthetic thermoplastics based on crude oil - Biodegradable (most important property) - PLA making via ionic polymerisation i.e. ring circular merging of two lactic acid molecules Lactid acid molecule -> polylacid acid; T 140 C 180 C and catalytic ZnO2; (source Wikipedia.)

4 Introduction Pros: Material properties: - high E-modulus (Young s ) - Scratch resistivity - High transparence - PET like properties - Smell barrier Cons: - Hydrophylic - Prone to hydrolysis - Slow crystallization -.

5 Introduction - Consumables - Packaging beakers - Foils - Medical appl. - Bottles (PET surr.) - foils t/a 2009 Much foil (source pics: Wikip; Fh-IPM)

6 Introduction NIR spectrum PLA CH x log(1/r) C-H X CH 3 OH H 2 O CH CH OH OH C=O OH Wellenlänge / nm Different applications -> material properies -> process conditions -> control -> in-line spectroscopy possible solution

7 Introduction

8 Equipment

9 Equipment Fibre coupled probes with saphire windows NIR Diode array (InGaAs ex.) 950 nm 2100 nm; Double twin screw extruder probes at die

10 Equipment

11 Measurements: Amount of Nanofill additiv 1,0 0,8 0% 0.01% 0.05% I mix /I PLA 0,6 0,4 0,2 0, wavelength / nm 0.1% 1% 3% T ( λ ) = I ( λ ) mix I ( λ ) PLA

12 Measurements: Nanofil, TiO 2 TiO 2 agglomerates TiO 2 nano particles nanofil TiO 2 and Nanoclay additives Control of amount; agglomeration processes

13 evaluation: Prediction model amount Nanofill based on NIR spectra 4,0 3,5 Predicted nanofil % (m/m) 3,0 2,5 2,0 1,5 1,0 0,5 0,0-0,5 0,0 0,5 1,0 1,5 2,0 2,5 3,0 reference nanofil % (m/m) PLSR prediction model of the nanofil content (0 3%) in PLA with a correlation better than Spectral pre-treatment: vector normalization. Validation method: cross validation. Reference filled in amount Nanofill into extruder.

14 Results: dynamic behaviour particles, additive 1 d ln T(λ) = τ = 1 d ln I ( λ ) = N C sca = A λ n Equ. (1) I O ( λ ) Meas. Theory C sca = A λ n Rayleigh approx. d << λ (d/ λ < 0.8) 0 n 4 n = 0 big particles n = 4 very small particles C sca : scattering cross sect, A = A(r particle,, refractive index particle, matrix, number particles)

15 Results -0,030-0,035-0,040 Messwerte PLA /Nano 0.05% Fit lnt = A / n -0,045 ln T -0,050-0,055-0,060-0,065 Chi^2/DoF = E-6 R^2 = A ± n ± ,070-0, wavelength / nm Spectral data PLA-0.05% nanofil with a least squares fit of the scattering relation according to equation 1.

16 Results nanofil content % A n

17 Results: particle radius τ λ = ln I T λ I 0 λ τ λ = ln I T λ I 0 λ τ λ = ln I T λ I 0 λ = NL C Ext r, m, λ p r, ρ, σ dr = NL C Ext r, m, λ p r, ρ, σ dr = NL C Ext r, m, λ p r, ρ, σ dr p r, ρ, σ : theor. particle distribution = log normal C Ext r, m, λ : scattering cross sec. Mie theory

18 Results: particle radius From REM calculation of equivalence average diameter τ λ = ln I T λ I 0 λ τ λ = ln I T λ I 0 λ = NL C Ext r, m, λ p r, ρ, σ dr = NL C Ext r, m, λ p r, ρ, σ dr REM measurement Comparision REM <-> Turbodimetric Probe Probe REM Turbidimetrie REM Turbidimetrie 784±379 nm 800 nm 1925±1857 nm 3340 nm

19 Results: particle radius mittl. Partikelradius [nm] τ λ = ln I T λ I 0 λ τ λ = ln I T λ I 0 λ = NL C Ext r, m, λ p r, ρ, σ dr = NL C Ext r, m, λ p r, ρ, σ dr mittl. Partikelradius Extruder Parameters (T, throughput, ) varied -> influenced particle radius Probenbezeichnungen

20 Results: fibre (flax) content in PLA 0,24 Reflectance PLA / Flachs 0,22 reflectance 0,20 0,18 0,16 0,14 0,12 0,10 0,08 τ λ = ln I T λ I 0 λ τ λ = ln I T λ I 0 λ 30% 25% 20% 15% 10% = NL C Ext r, m, λ p r, ρ, σ dr = NL C Ext r, m, λ p r, ρ, σ dr Spectrum dominated by Flax fibres 0,06 5% 0,04 0% 0,02 0, wavelength / nm PLA spectrum

21 Results: fibre (flax) content in PLA 0% τ λ = ln I T λ I 0 λ τ λ = ln I T λ 5% I 0 λ = NL C Ext r, m, λ p r, ρ, σ dr = NL C Ext r, m, λ p r, ρ, σ dr 25% 30% 10% 15% 20%

22 Results: fibre (flax) content in PLA Partial least square regression of flax content in PLA R 2 = 0.99

23 Results: fibre (flax) content in PLA Multivariate curve resolution (MCR) Estimated concentr. Δt No. of measurement Monitoring of process duration Δt and dispersion variability

24 Results: fibre (flax) content in PLA Estimated component spectra flax PLA wavelength / nm

25 Results: fibre (flax) content in PLA - Control of process duration - Prediction of fibre, additiv amount - Monitoring of dispersion stability, variability

26 Results: variation of extrusion parameters 4 max Elongation at break Effects Extrudertemp(A=BCE=DEF) Drehzahl(B=ACE=CDF) TiO2(C=ABE=BDF) Nanofill(D=AEF=BCF) Feuchte(E=ABC=ADF) Schnecke(F=ADE=BCD) AB=CE AC=BE AD=EF Factors AE=BC=DF AF=DE BD=CF BF=CD ABD=ACF=BEF=CDE ABF=ACD=BDE=CEF

27 Results: variation of extrusion parameters 150 Young s Modulus E-Modul 100 Effects Extrudertemp(A=BCE=DEF) Drehzahl(B=ACE=CDF) TiO2(C=ABE=BDF) Nanofill(D=AEF=BCF) Feuchte(E=ABC=ADF) Schnecke(F=ADE=BCD) AB=CE AC=BE Factors AD=EF AE=BC=DF AF=DE BD=CF BF=CD ABD=ACF=BEF=CDE ABF=ACD=BDE=CEF

28 Results: variation of extrusion parameters Young s Modulus

29 Conclusion Process- and material control with spectroscopic in-line measurements Material properties can be modified specifically in extrusion process

30 POWER CHANNEL1 CHANNEL2 SHUTTER h Future Work: reactive extrusion LA monomer -> PLA Reaction Control Prozeß- Spektrometer Turnover Lichtleitfasern 0 I MCS 511 NIR 1,7 MUX 502 CLH 500 Extruder Optische Sensoren Reaktionsverlauf

31 Thank you for your attention!