You dial in 300 microliters, weigh what comes out, and find 295. The obvious fix is to tell the instrument to aim a little higher. That adjustment, done properly across the whole volume range, is the correction curve, and it is how a class delivers the right volume rather than a consistently biased one.
What a correction curve is
A correction curve is a mapping from the volume you want to the volume the instrument actually targets to achieve it. If aiming for 300 delivers 295, the curve tells the instrument to aim for roughly 305 when you ask for 300. It corrects systematic error, the steady offset that biases every dispense the same way.
Why one point is not enough
The offset is rarely the same fraction at every volume. A class can be accurate at 300 microliters and low at 50, because retention and wetting do not scale linearly. Correcting only at your most common volume leaves the rest of the range biased. That is why a good curve uses several points across the range, each measured, so trueness holds from the smallest volume to the largest.
Building the curve
- Dispense replicates at several target volumes spanning your working range.
- Measure the delivered volume at each, by weighing or by reading a dye.
- Enter each target and its measured value as a point, so the class learns the real relationship.
- Add more points where accuracy matters most, then re-measure to confirm the curve holds.
For a stepped workflow like multi-dispensing, the curve is built the same way but against the actual volumes each step aspirates, not the aliquot size, because the tip empties progressively.
A caution about defaults
Predefined default classes usually cannot have their correction curve modified. That is deliberate, since they are shared baselines, but it means the moment you need a custom curve you must save your own copy and correct that. Trying to force a default is a common early frustration.