If you, like me, use Google® Scholar to follow multiple topics with email
alerts, I have a time saver tip for you.
TIP: When
you set up a Google® Scholar Alert, use the allintitle feature.
For example, when I set up a Google® Scholar Alert for dibenzothiophene, I used
the following procedure. NOTE: I use Internet Explorer. If you use a different
browser, things may look a little different. But the principles remain the
same.
In Google® Scholar (https://scholar.google.com), search for dibenzothiophene.
On the results page, click the triple bar in the upper left corner
Click Advanced search
Select in the title of the article
Click the magnifying glass in the upper right corner
On the results page, click Create alert
This creates an alert which is highly targeted to dibenzothiophene. I will miss
a number of items that mention dibenzothiophene in the abstract or the text.
But, since my goal is not to do a comprehensive literature search, that does
not matter.
What does matter is that I am able to stay on top of developments regarding
dibenzothiophene, without spending huge amounts of time doing so.
Happy New Year!
Showing posts with label TIPEMAIL. Show all posts
Showing posts with label TIPEMAIL. Show all posts
Tuesday, January 1, 2019
Monday, August 6, 2018
Add Value to Your Googling: Turn Your Search Into an eMail Alert
You search for stuff every day, sometimes all day. And sometimes, one of your
searches is so productive that you want to do it again.
For example, let’s say you perform the following Google® Scholar search, and you like what you see …
extractive desulfurization of model fuel
On the results page, in the left hand column (at least, that’s where I find it in Internet Explorer on my laptop) is a link labeled Create alert. Click on the link to create your alert. It’s that simple.
TIP: Be selective! It is so easy to create Google alerts, that you can easily find yourself wading through wads of search results that show up in your inbox every day. That has happened to me. I recommend pruning your alerts from time to time, as your interests change and your inbox fills up.
Here are examples of items I recently found in my inbox, resulting from two of my Google® Scholar alerts …
///////
Recent Results from Google® Scholar alerts using the following two search statements:
Aramco
"King Abdullah University" of Science and Technology
Development of modified siloxane membrane materials for enhanced NGL recovery from
natural gas
J. Yang* 1, D. Harrigan 1, J. Vaughn 1, M. Vaidya 2, V. Tammana 2
1 Aramco Services Company, USA, 2 Saudi Aramco, Saudi Arabia
Natural gas is of strategic interest to Saudi Aramco, including to reduce the Kingdom’s reliance
on liquid fuel for power generation and to provide for further economic growth and
diversification. However, many of the Kingdom’s natural gas reservoirs contain a complex gas
mixture (e.g. higher value hydrocarbons, acidic gases, inert gases, and trace components of
many other compounds). The higher hydrocarbon (C3+) removal from natural gas is required to
reduce the dew point and heating value of natural gas to pipeline specifications, prevent
condensation during transportation, and recover valuable C3+ hydrocarbons for use as chemical
feedstock.
Presently, Saudi Aramco is interested in developing new high flux and selective polymeric
membranes to remove C3+ hydrocarbon from raw natural gas. Silicone polymers, in particular
poly(dimethylsiloxane) (PDMS), have received considerable attention for this application
because they are cheaper, readily available, and have high intrinsic permeability to gases1-2.
Although commercial PDMS membranes can achieve C3+/methane separation in real gas
streams, improved gas selectivity while maintaining productivity would enhance profitability. In
this talk, a series of modified siloxane rubbery membrane materials that display higher
permeability and C3+/methane selectivity under single and or/ industrially-relevant feed streams
will be discussed, as compared to commercial PDMS membranes. The effect of polymer
structures and crosslinking and their impacts on membrane physical properties, permeation
performance, and membrane stability will be presented. Ultimately, this work will enable the
rapid development of novel rubbery membrane materials for enhanced C3+ hydrocarbon
removal from natural gas.
References:
1. Freeman, B. D. et al, “Separation of gases using solubility-selective polymers”, Trends
Polym. Sci., 5, 167-173 (1997).
2. Bake, R. W., “Membrane technology and applications”, 2nd Ed, John Wiley & Sons, New
York, 2004
3. Schultz, J. et al, “Membrane for separation of higher hydrocarbons from methane”, J.
Membr. Sci., 110, 37-45 (1996)
Keywords: Hydrocarbon removal, Rubbery membranes, Gas separation, Siloxane
source: https://elsevier.conference-services.net/viewsecurePDF.asp?conferenceID=4142&abstractID=972536
///////
Computer-Aided Gasoline Compositional Model Development Based on GC-FID Analysis
Chen Cui†‡, Linzhou Zhang*† , Yongjian Ma†, Triveni Billa‡, Zhen Hou‡, Quan Shi† , Suoqi Zhao† , Chunming Xu†, and Michael T. Klein*‡§
† State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, People’s Republic of China
‡ Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
§ Center for Refining and Petrochemicals, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Energy Fuels, Article ASAP
DOI: 10.1021/acs.energyfuels.8b01953
Publication Date (Web): July 24, 2018
Copyright © 2018 American Chemical Society
*E-mail: lzz@cup.edu.cn , *E-mail: mtk@udel.edu
Cite this:Energy Fuels XXXX, XXX, XXX-XXX
Abstract
The demand for improved gasoline product quality has helped make molecular-level models become more preferred for the modern refinery. Building the molecular compositional model is an essential first step for this quantitative molecular management of gasoline streams. Gas chromatography equipped with flame ion detection (GC-FID) is commonly used in the gasoline detailed hydrocarbon analysis (DHA). The combination of GC-FID analysis and molecular-level modeling is thus very attractive. In the present study, we developed a gasoline compositional model based solely on GC-FID as input. To suppress the negative influence of peak coelution, we developed a statistics-based peak tuning algorithm to obtain individual compound resolution at higher carbon number range. Using the tuned result as input, the molecular-level gasoline compositional model was built by estimating the quantitative percentages of the species in a predefined molecular library (573 molecules). The molecular-level compositional model has good extensibility and can link to the molecule-based physical properties prediction model. The model has been verified via applications on various gasoline samples. The prediction of research octane number for large-scale gasoline samples was also revealed.
source: https://pubs.acs.org/doi/abs/10.1021/acs.energyfuels.8b01953
///////
DeepSimulator: a deep Nanopore sequencing simulator
Yu Li 1, Renmin Han 1, Chongwei Bi 2, Mo Li 2, Sheng Wang 1*, Xin Gao 1*
1 Computational Bioscience Research Center (CBRC),
2 Biological and Environmental Sciences and Engineering (BESE) Division,
King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
* realbigws@gmail.com or xin.gao@kaust.edu.sa
Abstract
Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.
Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83\% to 97\%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.
Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator
source: https://www.ijcai-boom.org/uploads/5/1/6/8/51680821/sheng_wang.pdf
///////
Jean Steinhardt served as Librarian, Aramco Services, Engineering Division, for 13 years. He now heads Jean Steinhardt Consulting LLC, producing the same high quality research that he performed for Aramco.
Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online research
Email Jean at research@jeansteinhardtconsulting.com with questions on research, training, or anything else
Visit Jean’s Web site at http://www.jeansteinhardtconsulting.com/ to see examples of the services we can provide
For example, let’s say you perform the following Google® Scholar search, and you like what you see …
extractive desulfurization of model fuel
On the results page, in the left hand column (at least, that’s where I find it in Internet Explorer on my laptop) is a link labeled Create alert. Click on the link to create your alert. It’s that simple.
TIP: Be selective! It is so easy to create Google alerts, that you can easily find yourself wading through wads of search results that show up in your inbox every day. That has happened to me. I recommend pruning your alerts from time to time, as your interests change and your inbox fills up.
Here are examples of items I recently found in my inbox, resulting from two of my Google® Scholar alerts …
///////
Recent Results from Google® Scholar alerts using the following two search statements:
Aramco
"King Abdullah University" of Science and Technology
Development of modified siloxane membrane materials for enhanced NGL recovery from
natural gas
J. Yang* 1, D. Harrigan 1, J. Vaughn 1, M. Vaidya 2, V. Tammana 2
1 Aramco Services Company, USA, 2 Saudi Aramco, Saudi Arabia
Natural gas is of strategic interest to Saudi Aramco, including to reduce the Kingdom’s reliance
on liquid fuel for power generation and to provide for further economic growth and
diversification. However, many of the Kingdom’s natural gas reservoirs contain a complex gas
mixture (e.g. higher value hydrocarbons, acidic gases, inert gases, and trace components of
many other compounds). The higher hydrocarbon (C3+) removal from natural gas is required to
reduce the dew point and heating value of natural gas to pipeline specifications, prevent
condensation during transportation, and recover valuable C3+ hydrocarbons for use as chemical
feedstock.
Presently, Saudi Aramco is interested in developing new high flux and selective polymeric
membranes to remove C3+ hydrocarbon from raw natural gas. Silicone polymers, in particular
poly(dimethylsiloxane) (PDMS), have received considerable attention for this application
because they are cheaper, readily available, and have high intrinsic permeability to gases1-2.
Although commercial PDMS membranes can achieve C3+/methane separation in real gas
streams, improved gas selectivity while maintaining productivity would enhance profitability. In
this talk, a series of modified siloxane rubbery membrane materials that display higher
permeability and C3+/methane selectivity under single and or/ industrially-relevant feed streams
will be discussed, as compared to commercial PDMS membranes. The effect of polymer
structures and crosslinking and their impacts on membrane physical properties, permeation
performance, and membrane stability will be presented. Ultimately, this work will enable the
rapid development of novel rubbery membrane materials for enhanced C3+ hydrocarbon
removal from natural gas.
References:
1. Freeman, B. D. et al, “Separation of gases using solubility-selective polymers”, Trends
Polym. Sci., 5, 167-173 (1997).
2. Bake, R. W., “Membrane technology and applications”, 2nd Ed, John Wiley & Sons, New
York, 2004
3. Schultz, J. et al, “Membrane for separation of higher hydrocarbons from methane”, J.
Membr. Sci., 110, 37-45 (1996)
Keywords: Hydrocarbon removal, Rubbery membranes, Gas separation, Siloxane
source: https://elsevier.conference-services.net/viewsecurePDF.asp?conferenceID=4142&abstractID=972536
///////
Computer-Aided Gasoline Compositional Model Development Based on GC-FID Analysis
Chen Cui†‡, Linzhou Zhang*† , Yongjian Ma†, Triveni Billa‡, Zhen Hou‡, Quan Shi† , Suoqi Zhao† , Chunming Xu†, and Michael T. Klein*‡§
† State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, People’s Republic of China
‡ Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
§ Center for Refining and Petrochemicals, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Energy Fuels, Article ASAP
DOI: 10.1021/acs.energyfuels.8b01953
Publication Date (Web): July 24, 2018
Copyright © 2018 American Chemical Society
*E-mail: lzz@cup.edu.cn , *E-mail: mtk@udel.edu
Cite this:Energy Fuels XXXX, XXX, XXX-XXX
Abstract
The demand for improved gasoline product quality has helped make molecular-level models become more preferred for the modern refinery. Building the molecular compositional model is an essential first step for this quantitative molecular management of gasoline streams. Gas chromatography equipped with flame ion detection (GC-FID) is commonly used in the gasoline detailed hydrocarbon analysis (DHA). The combination of GC-FID analysis and molecular-level modeling is thus very attractive. In the present study, we developed a gasoline compositional model based solely on GC-FID as input. To suppress the negative influence of peak coelution, we developed a statistics-based peak tuning algorithm to obtain individual compound resolution at higher carbon number range. Using the tuned result as input, the molecular-level gasoline compositional model was built by estimating the quantitative percentages of the species in a predefined molecular library (573 molecules). The molecular-level compositional model has good extensibility and can link to the molecule-based physical properties prediction model. The model has been verified via applications on various gasoline samples. The prediction of research octane number for large-scale gasoline samples was also revealed.
source: https://pubs.acs.org/doi/abs/10.1021/acs.energyfuels.8b01953
///////
DeepSimulator: a deep Nanopore sequencing simulator
Yu Li 1, Renmin Han 1, Chongwei Bi 2, Mo Li 2, Sheng Wang 1*, Xin Gao 1*
1 Computational Bioscience Research Center (CBRC),
2 Biological and Environmental Sciences and Engineering (BESE) Division,
King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
* realbigws@gmail.com or xin.gao@kaust.edu.sa
Abstract
Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.
Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83\% to 97\%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.
Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator
source: https://www.ijcai-boom.org/uploads/5/1/6/8/51680821/sheng_wang.pdf
///////
Jean Steinhardt served as Librarian, Aramco Services, Engineering Division, for 13 years. He now heads Jean Steinhardt Consulting LLC, producing the same high quality research that he performed for Aramco.
Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online research
Email Jean at research@jeansteinhardtconsulting.com with questions on research, training, or anything else
Visit Jean’s Web site at http://www.jeansteinhardtconsulting.com/ to see examples of the services we can provide
Wednesday, March 21, 2018
Egg Samples: Results of Recent email Alerts
|
Further to a previous post [Keep Alert
(http://desulf.blogspot.com/2018/03/keep-alert-its-not-that-hard.html)]
on the value of creating email alerts, here are the results from two recent
email alerts that landed in my inbox.
/////// Results of a recent ScienceDirect (www.sciencedirect.com) email alert … Search Term Aramco High pressure sorption of various hydrocarbons and carbon dioxide in Kimmeridge Blackstone and isolated kerogen Original Research Article Fuel, Volume 224, 15 July 2018, Pages 412-423 Huangjing Zhao, Tianhao Wu, Abbas Firoozabadi |
|
GDI fuel sprays of light naphtha, PRF95 and gasoline using a piezoelectric injector under different ambient pressures Original Research Article Fuel, Volume 223, 1 July 2018, Pages 294-311 Zengyang Wu, Libing Wang, Jihad A. Badra, William L. Roberts, Tiegang Fang |
|
A minimalist functional group (MFG) approach for surrogate fuel formulation Original Research Article Combustion and Flame, Volume 192, June 2018, Pages 250-271 Abdul Gani Abdul Jameel, Nimal Naser, Gani Issayev, Jamal Touitou, Manik Kumer Ghosh, Abdul-Hamid Emwas, Aamir Farooq, Stephen Dooley, S. Mani Sarathy |
|
Shock tube measurements and kinetic modeling study on autoignition characteristics of cyclohexanone Original Research Article Combustion and Flame, Volume 192, June 2018, Pages 358-368 Jiuning He, Yudan Gou, Pengfei Lu, Changhua Zhang, Ping Li, Xiangyuan Li |
///////
Results of a recent Google® Scholar (https://scholar.google.com/) email alert … Search Term aramco
Methods and Apparatus for Collecting and Preserving Core Samples from a Reservoir
A Gupta, DT Georgi - US Patent App. 15/791,993, 2018
Abstract Provided herein are methods and apparatus for collecting and preserving
core samples from a reservoir. In some embodiments, a method includes obtaining
core samples from a reservoir using a rock and fluid sampling tool and depositing the
Results of a recent Google® Scholar (https://scholar.google.com/) email alert … Search Term aramco
Methods and Apparatus for Collecting and Preserving Core Samples from a Reservoir
A Gupta, DT Georgi - US Patent App. 15/791,993, 2018
Abstract Provided herein are methods and apparatus for collecting and preserving
core samples from a reservoir. In some embodiments, a method includes obtaining
core samples from a reservoir using a rock and fluid sampling tool and depositing the
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Durable and Self-Healing Cement Systems: Lab Testing and Field Deployment
AS Al-Yami, V Wagle, AA Santagati, S Taoutaou - Offshore Technology Conference …, 2018
Abstract Pressure and temperature changes during the life of the well could cause some
Casing-Casing annulus (CCA) pressure leaks in the annulus between 13 3/8" and 9
5/8" casings, and 13 3/8" and 18 5/8" casings. Such failures in wellbore isolation.
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Systems and methods for producing propylene
S Shaikh, A Jamal, Z Zhang - US Patent App. 15/798,818, 2018
… The resulting cracked molecules may have combined the same number of
carbon atoms as the original molecule prior to cracking. [0027]. Examples
of metathesis catalysts and cracking catalysts are disclosed in co-pending
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[HTML] Experimental research of condensate blockage and mitigating effect of gas injection
Z Wang, S Zhu, W Zhou, H Liu, Y Hu, P Guo, J Du… - Petroleum, 2018
The condensate blockage causes a substantial decrease in well productivity for gas
condensate reservoirs. Based on the previous studies, a novel experimental me.
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[PDF] 8 Another Egyptian Revolution
S Bowker
… 245–263. 3 For more information on the Street of the Tentmakers and
traditional applications of khay- amiya, see John Feeney, 'Tentmakers of
Cairo', Saudi Aramco World (1986) and Blaire O. Gagnon, 'Egyptian Appliques',
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An experimental investigation of the motion of long bubbles in high viscosity slug flow in horizontal pipes
KH Bendiksen, M Langsholt, L Liu - International Journal of Multiphase Flow, 2018
The slug bubble velocity is one of the most important closure relations in multiphase flow
models, and has been studied extensively for decades. Available model.
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