This document summarizes analytical tools for evaluating algal biodiesel production pathways. Algal biodiesel production can be represented in three steps: 1) growing algae, 2) harvesting and concentrating algae, and 3) extracting and upgrading lipids to biodiesel. The document describes various microscopy, spectroscopy, and chemical analysis techniques used to evaluate intermediate products at each step of algal biodiesel production.
David Glass Presentation at 2010 Algae Biomass SummitDavid Glass
Slides from a presentation given by David Glass, "Impact of Biotechnology Regulations on Use of Genetically Modified Algae in Biofuel Production", at the 2010 Algae Biomass Summit, Phoenix, AZ, September 28, 2010.
David Glass Presentation at 2010 Algae Biomass SummitDavid Glass
Slides from a presentation given by David Glass, "Impact of Biotechnology Regulations on Use of Genetically Modified Algae in Biofuel Production", at the 2010 Algae Biomass Summit, Phoenix, AZ, September 28, 2010.
The topic is captioned as Green genes- a promising fuel source for future..the ppt describes about biofuel and its forms..mainly focused on biodiesel and its present status, applications etc.,
Introduction
Evolution of biofuels
Biofuel production methods
Target areas for biotechnological interventions
Current research and developments
Success stories
Applications
Future line
Summary
Conclusion
Green genes
Green genes- plants and algae
Hydrocarbons, polysaccharides and triacylglycerides -precursors for biofuel
Biofuel
From renewable biological processes
Forms of biofuel:
Biodiesel
Bioethanol
Biomethane
Biohydrogen
Biodegradable and ecofriendly
Major sources- plants and algae
Evolution of biofuel
Biomethane
Agricultural waste, manure, plant material, green waste, etc.
Anaerobic digestion
Cooking
Compressed biomethane - vehicle
Biohydrogen
Source - algal biomass
Biological process – fermentation
Organic acid as substrate – higher fermentation rate
Fuel for vehicles
Bioethanol from lignocellulose biomass
Presence of lignin in vascular tissue - barrier
Enzymatic digestion of lignin - improve plant carbohydrate production
Genes encoding enzymes hydroxyphyl (H), guaiacyl (G) and syringyl (S) - building blocks of lignin
Antisense constructs to knock out genes encoding enzymes
…bioethanol from lignocellulose biomass
Mature stem harvested - late flowering stage
Plants with least lignin have high carbohydrate level
Hydroxycinnamoyl - highly contributes for lignin blocking than enzymes like C 3-H and C 4-H
C 4H : Cinnamate 4-hydroxylase
HCT : Shikimate hydroxycinnamoyl transferase
C 3-H : Coumaroyl shikimate 3-hydroxylase
CCoAOMT : Caffeoyl CoA 3-O-methyltransferase
F 5-H: Ferulate 5-hydroxylase
COMT: Caffeic acid 3-O-methyltransferase
Higher saccharification efficiency - transgenic lines
Pathway - conserved across plant kingdom
Targeted genes - candidate genes for improving saccharification in bioenergy crops like jatropha, switchgrass etc.
Biodiesel from algal biomass
Photosynthetic, heterotrophic organisms
Potential for cultivation as energy crops
Microalgal species with oil content
Why microalgae than plants?
More oil yield
Small area of land
Lesser need of labour, nutrients and water
Grow rapidly with high solar energy conversion efficiency
Wider adaptability
Current research and developments
Offshore Membrane Enclosure for Growing Algae (OMEGA) system
Success stories
Applications
The topic is captioned as Green genes- a promising fuel source for future..the ppt describes about biofuel and its forms..mainly focused on biodiesel and its present status, applications etc.,
Introduction
Evolution of biofuels
Biofuel production methods
Target areas for biotechnological interventions
Current research and developments
Success stories
Applications
Future line
Summary
Conclusion
Green genes
Green genes- plants and algae
Hydrocarbons, polysaccharides and triacylglycerides -precursors for biofuel
Biofuel
From renewable biological processes
Forms of biofuel:
Biodiesel
Bioethanol
Biomethane
Biohydrogen
Biodegradable and ecofriendly
Major sources- plants and algae
Evolution of biofuel
Biomethane
Agricultural waste, manure, plant material, green waste, etc.
Anaerobic digestion
Cooking
Compressed biomethane - vehicle
Biohydrogen
Source - algal biomass
Biological process – fermentation
Organic acid as substrate – higher fermentation rate
Fuel for vehicles
Bioethanol from lignocellulose biomass
Presence of lignin in vascular tissue - barrier
Enzymatic digestion of lignin - improve plant carbohydrate production
Genes encoding enzymes hydroxyphyl (H), guaiacyl (G) and syringyl (S) - building blocks of lignin
Antisense constructs to knock out genes encoding enzymes
…bioethanol from lignocellulose biomass
Mature stem harvested - late flowering stage
Plants with least lignin have high carbohydrate level
Hydroxycinnamoyl - highly contributes for lignin blocking than enzymes like C 3-H and C 4-H
C 4H : Cinnamate 4-hydroxylase
HCT : Shikimate hydroxycinnamoyl transferase
C 3-H : Coumaroyl shikimate 3-hydroxylase
CCoAOMT : Caffeoyl CoA 3-O-methyltransferase
F 5-H: Ferulate 5-hydroxylase
COMT: Caffeic acid 3-O-methyltransferase
Higher saccharification efficiency - transgenic lines
Pathway - conserved across plant kingdom
Targeted genes - candidate genes for improving saccharification in bioenergy crops like jatropha, switchgrass etc.
Biodiesel from algal biomass
Photosynthetic, heterotrophic organisms
Potential for cultivation as energy crops
Microalgal species with oil content
Why microalgae than plants?
More oil yield
Small area of land
Lesser need of labour, nutrients and water
Grow rapidly with high solar energy conversion efficiency
Wider adaptability
Current research and developments
Offshore Membrane Enclosure for Growing Algae (OMEGA) system
Success stories
Applications
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
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Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
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2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Orchestrator execution result
Defect reporting
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Have the message received by managers and peers along with a test email for review
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But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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Analytical tools for evaluating algal biodiesel production collin beal - may 2010
1. Analytical Tools for Evaluating Algal
Biodiesel Production
Beal C.M., Webber M.E., Ruoff R.S.,
Hebner R.E., Romanovicz D., and Connelly R.
ES2010-90277
May 21st, 2010
2. Algal biodiesel production can be
represented in three steps
Beal C.M. et al., “A Framework to Report the Production of Biodiesel from Algae,”
BioEnergy Research, In Review. Colin M Beal
ASME 2010
2 ES2010-90277
May 21, 2010
3. Algal biodiesel production can be
represented in three steps
Three commonly studied pathways are:
1) Transesterification of Algal Lipids
2) Thermochemical Conversion of Algal Biomass
3) Excretion of Oils by Genetically Modified Organisms
Colin M Beal
ASME 2010
3 ES2010-90277
May 21, 2010
4. Transesterification of Algal Lipids
Beal et al., “A Framework to Report the Production of Biodiesel from Algae,” BioEnergy
Research, In Review.
Colin M Beal
ASME 2010
4 ES2010-90277
May 21, 2010
5. The Center for Electromechanics at UT-
Austin has a multi-disciplinary team
I would like to acknowledge:
S. Briggs J. Jones M. Poenie
E. Chen L. Katz M. Randazzo
R. Connelly K. Kinney D. Schmid
J. Choi M. Montoya F. Seibert
M. Fountain B. Morrison J. Van Reet
R. Hebner R. Pearsall M. Werst
I would like to thank OpenAlgae for funding:
Colin M Beal
ASME 2010
5 ES2010-90277
May 21, 2010
6. Algae are grown in flasks,
reactors, tanks, and ponds
UT-CEM, 2009-2010
Colin M Beal
ASME 2010
6 ES2010-90277
May 21, 2010
7. Then, the algae are concentrated by ~100X
Multiple harvesting methods are
being evaluated:
1) Centrifugation
2) pH induced floc/defloc
3) Dissolved Air Flotation
4) Electro-flocculation
5) Filtration
6) Resin collection
7) Electro-wicking
UT-CEM, 2009-2010
Colin M Beal
ASME 2010
7 ES2010-90277
May 21, 2010
8. After harvesting, the cells are compromised
with electromechanical pulsing
• Electromechanical pulsing is a cost
effective technology developed at UT-CEM
UT-CEM, 2010
UT-CEM, 2010
Colin M Beal
ASME 2010
8 ES2010-90277
May 21, 2010
9. After harvesting, the cells are compromised
with electromechanical pulsing
• Electromechanical pulsing is a cost
effective technology developed at UT-CEM
Before After
UT-CEM, 2010
Beal C.M., and Romanovicz D., 2008
Colin M Beal
ASME 2010
9 ES2010-90277
May 21, 2010
10. An enhanced coalescence membrane is
used to extract the algal lipids
UT-CEM 2009, 2010
Colin M Beal
ASME 2010
10 ES2010-90277
May 21, 2010
11. The lipids are then upgraded to fuel
Algal lipids are converted to fatty acid methyl
esters, the primary component in biodiesel, by
transesterification.
Colin M Beal
ASME 2010
11 ES2010-90277
May 21, 2010
12. Evaluating the intermediate products
requires many analytical tools
Colin M Beal
ASME 2010
12 ES2010-90277
May 21, 2010
13. Evaluating the intermediate products
requires many analytical tools
Colin M Beal
ASME 2010
13 ES2010-90277
May 21, 2010
14. Generally, microscopy provides structural
information.
1) Light Microscopy
Colin M Beal
ASME 2010
14 ES2010-90277
May 21, 2010
15. Generally, microscopy provides structural
information.
1) Light Microscopy
Fluorescence microscopy using Nile Red dye
Beal C.M., 2008
Colin M Beal
ASME 2010
15 ES2010-90277
May 21, 2010
16. Generally, microscopy provides structural
information.
1) Light Microscopy
2) Scanning Electron Microscopy
- Useful for evaluating electromechanical lysing
Before
Colin M Beal
ASME 2010
16 Beal C.M. and Romanovicz D., 2008, 2010
ES2010-90277
May 21, 2010
17. Generally, microscopy provides structural
information.
1) Light Microscopy
2) Scanning Electron Microscopy
- Useful for evaluating electromechanical lysing
After
Colin M Beal
ASME 2010
17 Beal C.M. and Romanovicz D., 2008, 2010
ES2010-90277
May 21, 2010
18. Generally, microscopy provides structural
information.
1) Light Microscopy
2) Scanning Electron Microscopy
3) Transmission Electron Microscopy
Control Electrically Pulsed
Control: Compact, well-
LB ordered structures.
chloroplasts
G
LB Pulsed: Disordered,
C initiating apoptosis
CW
CW
Chloroplasts (C), Golgi (G),
Cell walls (CW) , Lipid
Bodies (LB)
Beal C.M. and Romanovicz D., 2008
Colin M Beal
ASME 2010
18 ES2010-90277
May 21, 2010
19. Usually, spectroscopy provides composition
analysis
1) UV-Vis Spectrophotometry
- Measure of chlorophyll absorption (a.u.)
- Absorption corresponds to algal density (g/L)
- Algal productivity calculated from changes in algal density
Grown Mass Productivity
Bidwell 1979
Colin M Beal
ASME 2010
19 ES2010-90277
May 21, 2010
20. Usually, spectroscopy provides composition
analysis
1) UV-Vis Spectrophotometry
2) Raman Spectroscopy
Algal Spectra
(Nitrogen Starved Chlorella and Neochloris)
Huang et al., “Micro-Raman Spectroscopy of Algae: Composition Analysis and
Fluorescence Background Behavior,” Biotechnology and Bioengineering, 2010.
Colin M Beal
ASME 2010
20 ES2010-90277
May 21, 2010
21. Usually, spectroscopy provides composition
analysis
1) UV-Vis Spectrophotometry
2) Raman Spectroscopy
Huang et al., “Micro-Raman Spectroscopy of Algae: Composition Analysis and
Fluorescence Background Behavior,” Biotechnology and Bioengineering, 2010.
Colin M Beal
ASME 2010
21 ES2010-90277
May 21, 2010
22. Usually, spectroscopy provides composition
analysis
1) UV-Vis Spectrophotometry
2) Raman Spectroscopy
3) Infrared Spectroscopy (IR)
IR has been used to:
1) Analyze nutrient status in algae
2) Identify algal species
- Some groups are using it for lipid analysis,
but it requires dry samples.
Colin M Beal
ASME 2010
22 ES2010-90277
May 21, 2010
23. Usually, spectroscopy provides composition
analysis
1) UV-Vis Spectrophotometry
2) Raman Spectroscopy
3) Infrared Spectroscopy (IR)
4) Nuclear Magnetic Resonance Spectroscopy (NMR)
Beal et al., “Lipid Analysis of Neochloris oleoabundans
by Liquid State NMR,” Biotechnology and
Bioengineering, 2010.
Colin M Beal
ASME 2010
23 ES2010-90277
May 21, 2010
24. Chromatography also provides composition
analysis, but requires extraction
1) High Performance (Pressure) Liquid Chromatography (HPLC)
Healthy Starved
Signal Signal
(a.u.) (a.u.)
Time Beal C.M., 2009 Time
Acquired by Poenie M.
(min) (min)
Colin M Beal
ASME 2010
24 ES2010-90277
May 21, 2010
25. Chromatography also provides composition
analysis, but requires extraction
1) High Performance (Pressure) Liquid Chromatography (HPLC)
2) Thin Layer Chromatography (TLC)
Polarity
Connelly R., 2009
Processing Samples Lipid Standards
Colin M Beal
ASME 2010
25 ES2010-90277
May 21, 2010
26. Chromatography also provides composition
analysis, but requires extraction
1) High Performance (Pressure) Liquid Chromatography (HPLC)
2) Thin Layer Chromatography (TLC)
3) Gas Chromatography – Mass Spectrometry (GC-MS)
CB062309b #673-677 RT: 6.85-6.87 AV:5 NL: 4.02E6
T: {0,0} + c EI det=450.00 Full ms [ 50.00-400.00]
55.1
100
67.0
74.0
95
90
83.1
85 87.0
MS fragmentation patter for FAME - Provides
80
75
97.1 molecular
70
98.1
information for
65
60 volatile samples.
Relative Abundance
Relative 55
50
110.1
Abundance 45 - Generally
(a.u.)
40
35
123.1
limited to
30
125.1
264.2 biodiesel, or
25
20
137.1
138.1 180.2
222.2 265.2 FAME derived
15
141.0
152.1
166.1
from algae
10
207.0 221.1
5 193.1 223.2 266.3 296.3
246.2 281.0 297.3
327.0 341.0 355.0 383.0 397.0
0
60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400
m/z
m/z
Colin M Beal
ASME 2010
26 ES2010-90277
May 21, 2010
27. Summary: Tricks (or tools) of the trade
Colin M Beal
ASME 2010
27 ES2010-90277
May 21, 2010
28. Questions?
Colin M Beal, MS
Graduate Student
Thermal/Fluids Systems Division
Mechanical Engineering
The University of Texas at Austin
cbeal@mail.utexas.edu
Webber Energy Group
Ruoff Nanoscience and Technology Group
and
The Center for Electromechanics
Colin M Beal
ASME 2010
28 ES2010-90277
May 21, 2010