POSTERS THE 100m RACE OF COMMUNICATION - A MINI LECTURE - Chalmers
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POSTERS THE 100m RACE OF COMMUNICATION A MINI LECTURE
• Examples of great posters
– and perhaps not so great ones
• How to be successful in your designs
• The five minute rule
• Chalmers guidelines
• Research posters – hints and tips
• Assessment of submitted posters
3/12/18 Chalmers 2
So – Who am I then?
• Mikael Terfors
• Resident Art Director of Chalmers
• Degree in Communication and
Graphic Design from KIAD – England
• Worked for almost 30 years in
Advertising and Graphic design
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What makes a great
poster great?
• It has a well defined purpose
• It has a well defined target audience
• It quickly communicates a message
• It may break some design rules
• It leaves a lasting impression and
changes the attitude/makes the
viewer take action
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A brief history of posters 3/12/18 Chalmers 5
Let’s start from the beginning (kind of) • Lithography (printing technique) was invented in 1798 • Start of mass-communication – 1880 • Toulouse-Lautrec (1864–1901) – La Belle Epoque 3/12/18 Chalmers 6
Is this the most famous poster of all times? • Possibly • Governments and political movements quickly saw the use of the poster • Lord Kitchener WWI-recruitment poster • 10.000 produced – only three survive • An estimated 2,67 million volunteered 3/12/18 Chalmers 7
The modernist movement and posters • Bauhaus • Rule-breaking • A desire to break with the past • Very geometric 3/12/18 Chalmers 8
1930s The golden age
• By this time printing had become
more efficient
• The advent of travel(posters)
• Adolphe Cassandre
• ”Normandie” 1935
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Propaganda
• 1930s was also a golden age for
political posters
• Strong imagery
• Strong and simple messages
3/12/18 Chalmers 11Films
• Film posters has long been at the
forefront of poster design
• Creative and innovative
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Postmodernism • Often, all about breaking the rules • Deconstructing the message • Delivering it in a “jumbled” state • Is it effective? 3/12/18 Chalmers 14
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Further inspiration • Art posters • Travel • Propaganda • Films 3/12/18 Chalmers 16
Designing for success 3/12/18 Chalmers 17
Message
• The research poster is a hybrid
between a written paper and an oral
presentation
• A great opportunity to interact
• Adapt for a mixed audience, not all
may be familiar with your field
3/12/18 Chalmers 18Message cont.
• Decide what really needs to be on
your poster
• Select perhaps three main findings
• Be tough on yourselves
• Think about what you want the reader
to take away from their read
• Catch your audience with a heading
or an image
3/12/18 Chalmers 19Flow
• Decide on a layout
• Don’t overcomplicate
• Find a logical flow
• Help your reader to gather
the information
• Make something stand out
even from a distance
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Colour
• Choose a colour scheme
• Don’t overdo this – 3 colours is
enough
• One main (background)
• One contrasting (signal)
• One more for charts (information)
• Certain colour combinations work
better than others…
3/12/18 Chalmers 22BLACK YELLOW 3/12/18 Chalmers 23
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Colour Complementary
• Three simple ways
Monochromatic
Analogous
3/12/18 Chalmers 25Readability • Don’t use too much text. • Keep the font large – preferably over 24pt • Use bullet-points and paragraphs to break the text up. 3/12/18 Chalmers 26
Readability cont. • Don’t use too wide lines. About 60 characters are optimal • Use Arial or Akzidenz-Grotesk (They were made for posterworks – and they are Chalmers) • Use Bold, Medium, CAPS, Italics to highligt different levels 3/12/18 Chalmers 27
Readability cont.
• Use different levels of reading
• Most important first – create an
interest (Headline)
• Main points stand out (Subheads)
• Give the body-text an introduction
(Preamble)
• Nitty-Gritty For the very interested
(Captions and notes)
3/12/18 Chalmers 28Images • Use images! • Good quality • Preferably – produce your own • Make sure you are allowed to use them! • Beware of “Google” 3/12/18 Chalmers 29
Images cont. • Save photos as jpg • Resolution between 150 and 300dpi • Web images are mostly too low and of poor quality 3/12/18 Chalmers 30
Charts and figures • Put in a bit of effort here – it will pay off! • Preferably use EPS files (Vector) • Failing this – use PNG at a high resolution • Be creative! 3/12/18 Chalmers 31
The poster is the beginning
• The conclusion is important
• You want your research to stand out
and stick to the readers mind
• You want your readers to take action
3/12/18 Chalmers 32Don’t • Use “bad” clipart 3/12/18 Chalmers 33
Don’t
• Put type over a complicated image
or background
3/12/18 Chalmers 34Don’t
• Be too “creative” in your choice
of colours
3/12/18 Chalmers 35Don’t
• Be too “creative” in your choice
of fonts either (Remember – Arial and
Akzidenz)
3/12/18 Chalmers 36Don’t • Forget to spell check! 3/12/18 Chalmers 37
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Actually, more like three five minute rules… • The viewer takes three seconds to decide if your poster is interesting • Hold the interest for five minutes • Take no more than five minutes to read (and understand) • Take five minutes and assess yourself – or let a collegue do it 3/12/18 Chalmers 39
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Chalmers has a strong
brand and a great reputation
• In Sweden, it’s one of the strongest
and most trusted
• Use this to your advantage
• Together we are stronger
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No Shadows! No Alterations! No Distortions! 3/12/18 Chalmers 44
Communications and Marketing
No Additions! No Re-colouring!
3/12/18 Chalmers 45Give it space! 3/12/18 Chalmers 46
Hierarchy Partner
• Chalmers always first!
• Partner logo can be on the same line,
but firmly separated!
• Preferably – put the other
logos/marks/names at the bottom
of the poster
Research Centre
Sponsor group
3/12/18 Chalmers 47Chalmers Typefaces • Akzidenz-Grotesk Family • Times 10 Family • Arial 3/12/18 Chalmers 48
Chalmers Guidelines
• Current can be found and
downloaded via “Insidan”
• New guidelines will be
released during april
• New Colour palette
• New Avancez-mark
• New name-marks for:
Departments
Areas of advance
Centres
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3/12/18
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Chalmers Guidelines
• There are poster-templates
(for those who whish)
• InDesign
• PPT
3/12/18 Chalmers 53Interesting research poster examples 3/12/18 Chalmers 54
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Assessment of poster submissions 3/12/18 Chalmers 59
Enzyme constraints in yeast and
their use in metabolic engineering
Benjamín J. Sáncheza, Cheng Zhangbc and Jens Nielsena
a
Department of Biology and Biological Engineering, Chalmers University of Technology
b
Science for Life Laboratory, KTH - Royal Institute of Technology
Contact:
c
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology bensan@chalmers.se
BACKGROUND USING THE MODEL TO INTEGRATE PROTEOMIC DATA
• Inside a biological microorganism, thousands of different reactions (a.k.a. cellular metabolism) are Predictions of constraint-based simulations are typically quite variable, given
occurring at the same time, catalyzed by hundreds of different enzymes. The main goal in metabolic that the ratio of metabolites/reactions leads to multiple optima. By combining
engineering is to modify this metabolism for improving production of a specific chemical of interest. A proteomic data in a simulation of yeast growing aerobically on glucose at 0.1 1/h,
proper mathematical model of metabolism could therefore aid in optimization and design of the system. we reduce variability of flux predictions by ~54% on average.
• Genome scale modelling (GEM)1 is a successful approach for modelling metabolism. However, it has a Glycerolipid metabolism 92%
major limitation: it assumes that the uptake rate of the substrate source (e.g. glucose) is what limits ~10 fold reduction in Steroid biosynthesis
Pentose phosphate pathway
75.1%
74.9%
fluxes 100% unconstrained Glycerophospholipid metabolism 66.6%
production. This is rarely the case; experimental yields are usually much lower than maximum theoretical Tyrosine metabolism
Glycolysis / Gluconeogenesis
66.2%
55.9%
yields2. Instead, what often happens is that there is a shortage in the amount of enzymes. Phenylalanine metabolism
Inositol phosphate metabolism
55%
54%
Fatty acid degradation 52.8%
1 Cysteine and methionine metabolism 50.3%
• Integration of enzyme constraints in a metabolic model would therefore give insight into cellular Only−Metabolic Model Galactose metabolism 48.3%
0.9 Enzyme−Constrained Model Sphingolipid metabolism 48%
capabilities at conditions of high enzymatic demand, such as yeast growing at a high growth rate, Lysine biosynthesis
Ubiquinone and other terpenoid−quinone biosynthesis
46.8%
45.9%
0.8
consuming non-typical carbon sources and/or overexpressing a specific pathway. However, GEMs so far Phenylalanine, tyrosine and tryptophan biosynthesis
Glutathione metabolism
43.8%
43.6%
0.7
Cumulative distribution
do not include a direct way of connecting enzyme levels to metabolic fluxes. Sulfur metabolism
Starch and sucrose metabolism
43%
39.8%
0.6 Fatty acid elongation 39.4%
Glyoxylate and dicarboxylate metabolism 38.8%
0.5 Selenocompound metabolism 36.7%
Fructose and mannose metabolism 36.5%
Arginine and proline metabolism 34.9%
0.4
DEVELOPED METHOD
Thiamine metabolism 33.9%
Pyruvate metabolism 33.7%
0.3 Tryptophan metabolism 32.3%
Here we expand a Saccharomyces cerevisiae’s (budding yeast) GEM3 to account for enzymatic constraints. 0.2
Nitrogen metabolism
Oxidative phosphorylation
32.3%
31.7%
This is done with the constraint-based approach extended to include enzymes as part of reactions, using
One carbon pool by folate 31.1%
Alanine, aspartate and glutamate metabolism 29.9%
0.1
Butanoate metabolism 27.9%
mass balances for both metabolites and enzymes4. By using this formalism, reaction fluxes v are limited 0 −4 −3 −2 −1 0 1 2 3
Amino sugar and nucleotide sugar metabolism
Pantothenate and CoA biosynthesis
26.4%
25.5%
by the maximum flux each enzyme is able to catalyze vmax, which can be estimated from the enzyme’s 10 10 10 10 10 10
Flux variability [mmol/gDWh]
10 10 beta−Alanine metabolism
Purine metabolism
24.6%
21.6%
abundance inside the cell [E] together with the enzyme’s turnover number kcat5. Valine, leucine and isoleucine degradation
Pentose and glucuronate interconversions
21.5%
17.6%
Glycine, serine and threonine metabolism 16.8%
Pyrimidine metabolism 16.5%
Enzyme-constrained model: Pathways across most parts of Lysine degradation
Propanoate metabolism
12.3%
11.6%
Only-metabolic model: Enzymes act as substrates in reactions metabolism are reduced, Histidine metabolism
Folate biosynthesis
9.9%
7.1%
Enzymes do not take part of reactions specially in phospholipid Citrate cycle (TCA cycle) 6.5%
A mass balance for E yields... Vitamin B6 metabolism 5.3%
metabolism Terpenoid backbone biosynthesis 5.3%
v = kcat· e ≤ kcat· [E] = vmax
Synthesis and degradation of ketone bodies 3%
e [mmol/gDW] Nicotinate and nicotinamide metabolism 3%
(constrained by E Fatty acid biosynthesis 2.3%
E proteomic data) Biotin metabolism 1.1%
A B Databases used for enzyme data:
v [mmol/gDWh]
A B PREDICTING GROWTH UNDER DIFFERENT MEDIA CONDITIONS
v [mmol/gDWh]
The expanded model predicts the growth rate at which the available protein runs
Stoichiometry: A + 1/kcat E � B out, which is for several media conditions6,7 the maximum growth rate.
Only-Metabolic Model Enzyme-Constrained Model
6 0.6
Minimal Media
Minimal Media + aas
ENZYME-CONSTRAINED MODEL OF S. CEREVISIAE 5 0.5 Complex Media (YEP)
Glucose
• The expanded model has 6768 reactions, 3406 metabolites (out of which 777 are enzymes) and 14
[1/h]
[1/h]
4 0.4 Fructose
Raffinose
compartments representing the most common cell organelles.
max
max
Sucrose
Maltose
Predicted µ
Predicted µ
3 0.3
• As seen below, enzymes are present in all existing compartments of the model, with the highest presence Mannose
Galactose
in cytoplasm, mitochondrion and endoplasmic reticulum. 2 0.2 Sorbitol
Trehalose
Ethanol
1 0.1
Acetate
Glycerol
0 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0 0.1 0.2 0.3 0.4 0.5 0.6
Experimental µmax [1/h] Experimental µmax [1/h]
PREDICTING PRODUCT YIELD
Succinate production in Farnesene production in
batch fermentations fedbatch fermentations
1.4
Only−Metabolic Model
0.3
Only−Metabolic Model The expanded
Enzyme−Constrained Model Enzyme−Constrained Model
1.2 Experimental Data
0.25 Experimental Data model constraints
Glucose Phase
Ethanol Phase yield predictions
1
0.2 without losing
Farnesene Yield [g/g]
Succinate Yield [g/g]
Enzymes 0.8 biological
Cell envelope 0.15
Cytoplasm 0.6 meaningful
Extracellular media 0.1 solutions when
0.4
Mitochondrion compared to
Nucleus
Peroxisome
0.2 0.05
experimental
Endoplasmic reticulum 0 0
data8,9.
0 0.2 0.4 0.6 0.8 0 0.1 0.2 0.3 0.4 0.5 0.6
Golgi Biomass Yield [gDW/g] Biomass Yield [gDW/g]
Lipid particle
Vacuole
Endoplasmic reticulum membrane
Vacuolar membrane PREDICTING KNOCKOUT PHYSIOLOGY OUTLOOK
Golgi membrane The expanded model correctly predicts the • By using enzyme
Mitochondrial membrane shift in the critical growth rate when NDI1 is constraints in a GEM of
knocked-out10. S. cerevisiae we gain
25
O 2 Consumption (WT strain) insight into the enzyme
Physico-chemical properties of enzymes in model: CO 2 Production (WT strain) cost of metabolic
1 1
20 O 2 Consumption (ndi1∆) The model is pathways under different
CO 2 Production (ndi1∆)
reliable for
0.8 0.8 conditions.
Flux [mmol/gDWh]
Classification of enzymes: 15 predicting the
Cumulative distribution
Cumulative distribution
effect of
0.6 0.6 • 183 enzymes from carbohydrate and energy • This way we can unveil
genetic
primary metabolism (lightest and fastest group) 10 the most likely key steps
modifications
0.4 0.4
• 222 enzymes from aminoacid, fatty acid and in the system that limit yeast growth
nucleotide primary metabolism (intermidiate group) 5
and/or metabolite
0.2 0.2
• 329 enzymes from intermediate and secondary production, for improving
metabolism (heaviest and slowest group)
0
0 20 40 60 80 100 120 140
0
10
−1
10
0
10
1
10
2
10
3
10
4
0
0.1 0.15 0.2 0.25 0.3 0.35 0.4 strain design.
Molecular weight [kDa] k [1/s] Dilution Rate [1/h]
cat
References
1. B.J. Sánchez & J. Nielsen, Integr. Biol. 2015, 7:846-858 6. J.P. van Dijken et al., Enz. Microbiol. 2000, 26:706-14
2. D.K. Ro et al., Nature 2006, 440:940-943 7. C.B. Tyson et al., Journal of Bacteriol. 1979, 138:92-98
3. H.W. Aung et al., Ind. Biotechnol. 2013, 9:215-228 8. A.M. Raab et al., Metabolic Eng. 2010, 12:518-525
4. E. O’Brien et al., Mol. Syst. Bio. 2013, 9:693 9. S. Tippmann et al., Biotech. & Bioeng. 2016, 113:72-81
5. A. Nilsson & J. Nielsen, Sci. Rep. 2016, 6:22264 10. B.M. Bakker et al., Journal of Bacteriol. 2000, 182:4730-4737
3/12/18 Chalmers 60Protein containing lipid bilayers intercalated
with size-matched mesoporous silica thin films
Simon Isaksson*, Erik B. Watkins, Kathryn L. Browning, Tania Kjellerup Lind,
Marité Cárdenas, Kristina Hedfalk, Fredrik Höök, Martin Andersson*
*Division of Applied Chemistry, Chalmers University of Technology
We tailored the pore size and pore
ordering of a mesoporous silica thin
film to match the native cell-membrane
arrangement of the transmembrane
protein human aquaporin 4. Using
neutron reflectivity, we provide
evidence of how substrate pores host
the bulky water-soluble domain of the
protein, which is shown to extend
7.2 nm into the pores of the silica
substrate (Figures 1 & 2). Figures 3
and 4 present interesting properties
of the two intercalated components.
Protein-silica intercalation
Figure 1: Illustration showing the achievement of
this work: Straightforward protein-silica intercalation.
Proteoliposomes Mesoporous silica
Figure 2: (A) Component volume fractions for
the SLB on a nonporous Si substrate as
determined by neutron reflectivity. The illustration
visualizes the data for the POPC (brown) bilayer
Figure 4: (A) TEM micrograph showing on nonporous silica (grey) with a protein (blue)
Figure 3: (A) Cryo-TEM micrograph of hexagonal ordering of the porous network in containing proteoliposome co-adsorbed on top of
a liposome sample. (B) Cryo-TEM the mesoporous silica substrate. The pores are the bilayer. (B) Volume fractions for the protein
micrograph of a proteoliposome sample. uniform with a diameter of 6 nm. (B) containing supported lipid bilayer on a
(C) Bilayer thickness distribution of 30 liposomes as SAXS data providing evidence of long range
determined from liposome samples,
mesoporous substrate. The protein
hexagonal pore ordering in the protruded 7.2 ± 1.0 nm into the porous
showing one population centered at mesoporous silica substrate. (C) Top view substrate and 10.0 ± 1.0 nm into the bulk when
5nm. (D) Bilayer thickness distribution of 30 of the mesoporous silica surface captured facing the substrate and away from the substrate,
proteoliposomes as determined from by SEM. Pores with pore diameters of about respectively. The illustration visualizes the NR data
proteoliposome samples, showing two 6 nm are accessible from the surface. for the POPC (brown) bilayer containing protein
populations centered at 5 and 7nm, respectively. (D) SEM cross section of mesoporous silica thin film (blue) intercalated with mesoporous silica (grey).
(highlighted in blue) deposited on a glass slide. The
mesoporous thin film is about 300 nm thick.
3/12/18 Chalmers 61Characterization of Cu-Exchanged Zeolites for Direct
Conversion of Methane to Methanol (DCMM)
Xueting Wang, Magnus Skoglundh, Anders Hellman and Per-Anders Carlsson,
Competence Centre for Catalysis, Chalmers University of Technology, Göteborg, Sweden
ADVANTAGES CHALLENGES
RESEMBLANCE TO MMO* ACTIVATION
Porous structure of ZSM-5 Copper Catalyst needs to be activated under higher
Various Cu species 29 temperature with oxidants before catalytic reaction.
Cu
*MMO (methane monooxgenase), an enzyme
that is capable of oxidizing methane to
Sirajuddin S. et al., J. Bio.
Chem. (2014)
methanol at ambient temperature and pressure. ACTIVE SITES
Under discussion.
Minority Cu species are
INORGANIC STRUCTRUE responsible for DCMM. S. Grundner et al., Nature
Advantage in manufacture, design, reaction J.S. Woertink et al., Commun., (2015)
PNAS, 106 (2009)
condition and modelling.
DCMM
Cu-exchanged zeolites EXTRACTION
Methanol needs to be extracted by water/ethanol.
CO + H2
CHARACTERIZATION FOR COPPER SPECIES
UV-vis spectroscopy Methanol-TPD (temperature programmed desorption)
Adsorption band at 22700 cm-1 relating to Cu species is related to: Methanol-TPD contains info about Cu species:
proposed active sites.* • Ion-exchange level • Number of sites
Missing band in the Cu-ZSM-5 samples. • Pre-treatment condition • Type of sites
10 Pre-oxidized Methanol-TPD Pre-reduced Methanol-TPD
SAR 27
8 Pre-treatment Adsorption Desorption Pre-treatment Adsorption Desorption
Kubelka Munk
6 SAR 55 O2 10% MeOH 0.08% O2 10% H2 1.5% MeOH 0.08%
22700 cm-1
4
2 SAR 330
0
5000 10000 15000 20000 25000 30000
Temperature
Wavenumber (cm-1)
Time
Sample: Cu-ZSM-5 with various SAR (silica alumina ratio)
Ion-exchange time: 24 h
Methanol-TPD for Cu-ZSM-5 Methanol-TPD for Cu-ZSM-5 (24h)
*Bezins et al., Catalysis Letters, 138 (2010), 14-22 different ion-exchange time different pre-treatment
500 1.5h 400
400 6h 1. Pre-oxidized
Infrared spectroscopy
Methanol (ppm)
300
MeOH (ppm)
2. Pre-reduced
300 12h
Accessible Cu+ and Cu2+ species. 200 3. Pre-oxidized
200
0h Number indicates
24h 100 the experiment
Intensity (arb. u.)
100 order
NO+ on Cu2+
0 0
NO- on Cu+ 75 175 275 75 175 275
Temperature (°C) Temperature (°C)
2000 1900 1800 1700
Wavenumber (cm-1) ACKNOWLEDGEMENT
Sample: Cu-ZSM-5 SAR 27 This work has been carried out within the project “Time-resolved in situ methods for design of catalytic sites within
Ion-exchange time: 24 h sustainable chemistry ”, Swedish Research Council (349-2013-567) and partly within Competence Centre for
Pretreatment: with O2 @ 550 °C for 1 h Catalysis, which is hosted by Chalmers University of Technology and financially supported by the Swedish Energy
Measurement: during NO exposure Agency and the member companies AB Volvo, ECAPS AB, Haldor Topsøe A/S, Scania CV AB, Volvo Car Corporation
AB, and Wärtsilä Finland Oy.
Competence Centre for Catalysis
3/12/18 Chalmers 623/12/18 Chalmers 63
Towards recycling of textile fibers
-solid state analysis of textile fibers and blends
Anna Petersonab, Hanna de la Mottea, Alexander Idströmc, Anna Palmed
Harald Brelide, Lars Nordstiernab
a
SP Technical Research Institute of Sweden
b
Division of Applied Chemistry, Chalmers University of Technology
c
Swerea IVF
d
Division of Chemical Engineering, Chalmers University of Technology
e
Södra Innovation
The global demand for textile materials is steadily increasing due to world population growth and the overall improvement of living standards. At the same time the
life cycle of textiles is shortened, generating more textile waste1. Recycling of textile fibers is a challenging subject but the environmental benefits from a working re-
cycling chain are substantial since textiles is one of the types of wastes associated with the
heaviest climate effects since it demands large amounts of water, pesticides and energy in the production2. An efficient recycling chain needs to meet the needs of
collecting, sorting and regenerating new fibers of high quality. In order to introduce an efficient recycling of fibers, characterization of the fiber content as well as
separation of fibers from material blends are crucial. It is thus of high interest to find ways to analyze the content of material blends as well as methods to physically
separate the fibers.
PATHWAYS FOR TEXTILE WASTE AUTOMATIC SORTING OF TEXTILES
An analytic method for the sorting of textile materials has to meet certain
characteristics:
• Ability to qualitatively distinguish between the fibers
Post-consumer Post-industrial
• Ability to quantify fiber content in fiber blends
Fibrous • High throughput
waste
Vibrational spectroscopy methods: Infrared and Raman
Collection Band frequency (cm-1) Type of vibration Fiber type
and 3 7 0 0 -3 2 0 0 O-H stretch (free and bonded) Cellulosics
sorting 2 9 3 0 -2 8 4 0 C-H stretch Most fibrous polymers
2 2 6 0 -2 2 4 0 C N stretch (saturated nitrile) Acrylics
1 7 4 0 -1 7 1 5 C=O stretch (esters) Polyesters, acrylics
1 6 7 0 -1 6 3 0 C=O stretch (amide) Polyamide, wool
Reuse Recycle Dispose 1 6 5 0 -1 5 1 5 N-H deform (amines, primary Wool, polyamide
and secondary)
Table 1. IR band frequencies for vibrations typical for textile fibers
Only one of the common textile fibers analyzed in the trial showed infrared
Mechanical Chemical Energy absorbance at a distinct wavelength. Hence detection cannot be limited to
recycling recycling recovery specific peaks for specific samples but has to be done by matching the full
spectrum of the textile samples against a spectral library.
Raman spectroscopy was performed with incident light of 732 and 532 nm.
Both wavelengths produced significant flourescence which interferes severly
with the spectra generated by Raman scattering. At the chosen wavelengths
SEPARATION OF FIBER BLENDS Raman is insufficient as an automatic sorting method.
Alkaline hydrolysis of polyester from a cotton/polyester blended fabric. The Solid state nuclear magnetic resonance
reaction is aided by a phase transfer catalyst to obtain mild reaction condi-
tions.
Reaction products are pure terephtalic acid, ethylene glycol and a cotton
residue whose physical properties such as crystalline structure and degree • Not suitable as an
of polymerization are dependant on reaction conditions. The parameters of on-line technique
highest influence were found to be alkali concentration and temperature.
• Can provide
detailed structural in-
formation on
fibrous samples.
Figure 2. Solid-state NMR spectrum of blended textile shreds from SOEX, with assignments.
Future work
Further analyses on the cotton residue, such as SEM and molecular weight
distribution. Trials with upscaling of the separation of the cotton/polyester
blend. Investigate Near-infrared spectroscopy as a possible automatic sorting
method.
Figure 1. ATR-IR spectra of cotton residue accentuating the transformation between cellulose I and cellulose II. Purple
References
1. Lu, J. J. & Hamouda, H. Current Status of Fiber Waste Recycling and its Future. Adv. Mater. Res. 878, 122–131 (2014).
traces, cotton residue showing absorbance at frequencies charceristic for cellulose II. Blue trace, cotton residue lack- 2. Naturvårdsverket. Avfallsstatistik för bättre miljöarbete (2012).
ing absorbance at frequencies characterstic for cellulose II.
3/12/18 Chalmers 64Useful links and software
• Adobe CC suite
• InDesign
• Photoshop
• Illustrator
• Corel Draw
• Microsoft Powerpoint
• Microsoft Paint (freeware)
• “Free” and Royaltyfree images and graphics
www.pixelbay.org www.shutterstock.com
www.123rf.com www.wikipedia.com
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