![](data:image/jpeg;base64,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)
Moisture Analyzer DAB 100-3 is laboratory measuring instrument designed to determine water content (relative humudity content ) in relative small samples of various substances. Excellence performance for a wide variety of applications in multiple industries such as food, environmental, chemical, pharmaceutical, etc.
Features
|
Model : DAB 100-3 |
Weighing range : 110g
Readability : 0.001 g
|
Moisture content : 0 to 100%
Moisture Readout : 0.01%
Reproducibility of sample weight 2g : 0.5%
Reproducibility of sample weight 10g : 0.02%
|
Temperature range : up to 199°C
|
Drying modes : Standard drying, Gentle drying, Pre-heat level
Moisture = Weight loss : 0 to 100%
Dry mass = Residual weight : 100 to 0%
|
Auto switch off options : 3 modes ( manual, automatic, time defined ) |
Heating module : Halogen Quartz Glass Heater |
|