Laboratory Scale Testing
Laboratory Scale Testing - Related Videos
09:54
Laboratory Scale Production and Purification of a Therapeutic Antibody
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Cited by 9 •
2017
This protocol describes the production of a therapeutic antibody in a mammalian expression system. The methods described include preparation of vector DNA, stable transfection and serum-free adaptation of a human embryonic kidney 293 cell line, set up of large scale cultures and purification using affinity chromatography.
13:36
Synthetic Spider Silk Production on a Laboratory Scale
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Cited by 16 •
2012
Despite the outstanding mechanical and biochemical properties of spider silks, this material cannot be harvested in large quantities by conventional means. Here we describe an efficient strategy to spin artificial spider silk fibers, which is an important process for investigators studying spider silk production and their use as next-generation biomaterials.
10:49
Power Input Measurements in Stirred Bioreactors at Laboratory Scale
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Cited by 9 •
2018
The power input in stirred bioreactors can be measured through the torque that acts on the impeller shaft during rotation. This manuscript describes how an air bearing can be used to effectively reduce friction losses observed in mechanical seals and improve the accuracy of power input measurements in small-scale vessels.
06:52
Laboratory Scale Slow Cook-Off Testing of Rocket Propellants: The Combustion Rate Analysis of a Slowly Heated Propellant (CRASH-P) Test
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2021
We present a protocol for a laboratory-scale slow cook-off test for solid rocket propellants called the Combustion Rate Analysis of a Slowly Heated Propellant (CRASH-P) test. Confined rocket propellants are slowly heated until autoignition, and both the cook-off temperature and the violence of reaction are measured with dynamic pressure sensors.
06:00
Experimental Protocol for Using Drosophila As an Invertebrate Model System for Toxicity Testing in the Laboratory
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Cited by 10 •
2018
In this paper, we provide a detailed protocol for exposing species in the genus Drosophila to pollutants with the goal of studying the impact of exposure on a range of phenotypic outputs at different developmental stages and for more than one generation.
FAQs
Researchers reproduce the key operating conditions that a material, component, process, or system will encounter, then vary selected parameters in a controlled way. This approach separates the effects of individual changes and makes performance comparisons more meaningful. The resulting data can reveal which conditions influence efficiency, stability, safety, or product quality before larger engineering systems are built.
Changing selected parameters helps identify relationships between operating conditions and measured performance. Researchers can determine whether a design limitation, efficiency change, stability problem, safety concern, or product-quality variation is associated with those conditions. This evidence supports process optimization and helps distinguish promising alternatives from options that may perform poorly during later scale-up.
Competing materials, components, processes, or system designs can be assessed under comparable laboratory conditions. Researchers monitor the same relevant outcomes, such as efficiency, stability, safety, or product quality, and use the results to identify differences in performance. This comparison provides an evidence base for selecting an approach before committing resources to pilot or industrial construction.
A typical workflow establishes the laboratory conditions, selects the parameters to vary, operates the small-scale setup, and monitors defined performance measures. Researchers then analyze outcomes to identify limitations, compare alternatives, and determine whether the process or design should be optimized. The findings guide decisions about whether further development or scale-up is justified.
The setup should reproduce the key operating conditions relevant to the intended engineering application rather than attempting to duplicate every feature of a full-scale system. Measurements should match the evaluation goals, including efficiency, stability, safety, or product quality. Selecting conditions and outcomes carefully improves the usefulness of the data for design assessment and process optimization.
It is especially valuable before pilot or industrial systems are constructed, when design choices, process conditions, or material options still need evaluation. Small-scale experiments can identify limitations and generate performance data with fewer resource requirements than full-scale implementation. Engineers can then use the evidence to reduce development risk, refine the design, and make better-informed scale-up decisions.