Column chromatography is a technique used to separate chemical compounds in a mixture based on how they interact differently with the stationary and mobile phases in the column. Compounds move through the column at different rates depending on their affinity for the stationary phase, allowing them to elute from the column in separate fractions over time. This technique can be used on both small and large scales to purify compounds for use in experiments by exploiting differences in their adsorption properties.
• Chromatography is a method of separation in which the components to be separated are distributed between two phases, one of these is called a stationary phase and the other is a mobile phase which moves on stationary phase in a definite direction
• Chromatography is a method of separation in which the components to be separated are distributed between two phases, one of these is called a stationary phase and the other is a mobile phase which moves on stationary phase in a definite direction
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. Column Chromatography
In chemistry, Column chromatography is a technique which is
used to separate a single chemical compound from a mixture
dissolved in a fluid. It separates substances based on
differential adsorption of compounds to the adsorbent as the
compounds move through the column at different rates
which allow them to get separated in fractions. This
technique can be used on small scale as well as large scale to
purify materials that can be used in future experiments. This
technique.
adsorption chromatography
method is a type of
2. Column Chromatography Principle
•
When the mobile phase along with the mixture that needs to be
separated is introduced from the top of the column, the movement of
the individual components of the mixture is at different rates. The
components with lower adsorption and affinity to stationary phase
travel faster when compared to the greater adsorption and affinity
with the stationary phase. The components that move fast are
removed first whereas the components that move slow are eluted out
last.
•
The adsorption of solute molecules to the column occurs in a
reversible manner. The rate of the movement of the components is
expressed as:
•
Rf = the distance travelled by solute/ the distance travelled by solvent
•
Rf is the retardation factor.
3. Column Chromatography
•
Column Chromatography is another common and useful separation technique in organic
chemistry. This separation method involves the same principles as TLC, but can be applied to
separate larger quantities than TLC. Column chromatography can be used on both a large and
small scale. The applications of this technique are wide reaching and cross many disciplines
including biology, biochemistry, microbiology and medicine. Many common antibiotics are
purified by column chromatography. To understand to uses of this separation technique, we can
use the last experiment as an example. In the TLC experiment, we separated and analyzed the
different components that makeup over-the-counter painkillers. The technique of TLC was useful
in determining the type and number of ingredients in the mixture, but it was not helpful for
collecting the separated components. We could only separate and visualize the spots. If we
needed to collect the separated materials, column chromatography could be used. We could
load 100 mg of a crushed Anacin tablet on a column made up of a silica stationary phase and
separate the aspirin from the caffeine and collect each of these compounds in separate beakers.
Column chromatography allows us to separate and collect the compounds individually. In this
experiment, Column Chromatography (abbreviated CC) will be used to separate the starting
material from the product in the oxidation of fluorene to flourenone and TLC will be used to
monitor the effectiveness of this separation.
4. Choosing a Stationary Phase
•
As with TLC, alumina and silica are the two most popular stationary phases in column
chromatography. For these common phases, the partitioning works in an analogous
manner. The more polar sample will be retained on the stationary phase longer. Thus the
least polar compound will elute from the column first, followed by each compound in
order of increasing polarity. PreLab Exercise Do not forget these additional sections in
your PreLab. Alumina and silica 106 Although the interactions between the mobile and
stationary phase are based on the same principles for CC and TLC, be careful when
predicting the order of elution. Since the direction of the solvent flow in TLC moves up
and in CC the solvent flows down, it appears that the order is “upside-down”. In TLC the
more polar molecules will have lower Rf values, but in CC they will be retained longer on
the column.Remember this when considering the polarities of the stationary phase as
well as the polarity of the compounds being separated when predicting the order of
elution. Stationary phases for CC can come in a variety of sizes, activities, acidic and basic
variations for both alumina and silica. The types of stationary phase chosen are
determined experimentally, or often based on results from a previous TLC experiment.
The type of adsorbent, the size of the column, the polarity of the mobile phase as well as
the rate of elution all affect the separation. These conditions can be manipulated to get
the best separation for your mixture.
5. Choosing Solvents Solvent
•
systems for use as mobile phases in CC can be determined from previous
TLC experiments, the literature, or experimentally. Normally, a separation
will begin by using nonpolar or low polarity solvent, allowing the
compounds to adsorb to the stationary phase, then SLOWLY switching the
polarity of the solvent to desorb the compounds and allow them to travel
with the mobile phase. The polarity of the solvents should be changed
gradually. On a macroscale, the mixing of two solvents can create heat and
crack the column leading to a poor separation. Some typical solvent
combinations are ligroin-dichloromethane, hexane-ethyl acetate and
hexane-toluene. Often an experimentally determined ratio of these
solvents can sufficiently separate most compounds. Solvents such as
methanol and water are normally not used because they can destroy the
integrity of the stationary phase by dissolving some of the silica gel.
6.
7. Type of column chromatography
•
There are two forms of column chromatography.
•
Liquid chromatography (LC)
•
Gas chromatography (GC)
•
The most widely used forms of column chromatography are:
•
Adsorption chromatography
•
Partition chromatography
•
Ion exchange chromatography
•
Gel chromatography
8. Types of Column Chromatography
•
:
•
1. Adsorption column chromatography – Adsorption chromatography is a
technique of separation, in which the components of the mixture are
adsorbed on the surface of the adsorbent.
•
2. Partition column chromatography – The stationary phase, as well as
.
partition chromatography
mobile phase, are liquid in
•
3. Gel column chromatography – In this method of chromatography, the
separation takes place through a column packed with gel. The stationary
phase is a solvent held in the gap of a solvent.
•
4. Ion exchange column chromatography – A chromatography technique in
which the stationary phase is always ion exchange resin.
9. Preparation of the Column
•
The column mostly consists of a glass tube packed with a suitable
stationary phase.
•
A glass wool/cotton wool or an asbestos pad is placed at the botton
of the column before packing the stationary phase.
•
After packing, a paper disc kept on the top, so that the stationary
layer is not disturbed during the introduction of sample or mobile
phase.
10. Types of preparing the column
•
There are two types of preparing the column, they are:
•
1. Dry packing / dry filling
•
In this the required quantity of adsorbent is poured as fine dry powder in
the column and the solvent is allowed to flow through the column till
equilibrium is reached.
•
2. Wet packing / wet filling
•
In this, the slurry of adsorbent with the mobile phase is prepared and is
poured into the column. It is considered as the ideal technique for packing.
11. •
B. Introduction of the Sample
•
The sample which is usually a mixture of components is dissolved in minimum
quantity of the mobile phase.
•
The entire sample is introduced into the column at once and get adsorbed on
the top portion of the column.
•
From this zone, individual sample can be separated by a process of elution.
•
C. Elution
•
By elution technique, the individual components are separated out from the
column.
•
It can be achieved by two techniques:
•
Isocratic elution technique: Same solvent composition or solvent of same
polarity is used throughout the process of separation.
•
Eg. Use of chloroform alone.
12. •
Gradient elution technique: Solvents of gradually ↑ polarity or ↑
elution strength are used during the process of separation.
•
E.g. initially benzene, then chloroform, then ethyl acetate then
chloroform
•
D. Detection of Components
•
If the compounds separated in a column chromatography procedure
are colored, the progress of the separation can simply be monitored
visually.
•
If the compounds to be isolated from column chromatography are
colorless.
•
In this case, small fractions of the eluent are collected sequentially in
labelled tubes and the composition of each fraction is analyzed by
TLC.
13. Applications
•
Column chromatography is one of the most useful methods for the
separation and purification of both solids and liquids. Its major
application includes:
•
Column Chromatography is used to isolate active ingredients.
•
It is very helpful in Separating compound mixtures.
•
It is used to determine drug estimation from drug formulations
•
It is used to remove impurities.
•
Used to isolation metabolites from biological fluids.
14. Advantages
•
Any type of mixture can be separated by column chromatography.
•
Any quantity of the mixture can also be separated.
•
Wider choice of mobile phase.
•
In preparative type, the sample can be separated and reused.
•
Automation is possible.