Intelligent instruments have become the core tools for accurate detection in modern laboratories, but their complexity brings operational challenges. According to data from the International Federation of Clinical Chemistry, 68.2% of laboratory errors stem from improper operation. Among these, errors in the pre-analytical phase account for the highest proportion (68.2%), while those in the analytical phase account for 13.3%. Standardized operations and systematic training are crucial for improving data reliability.

Four Core Misunderstandings in the Use of Intelligent Instruments

1.Over-reliance on automation and neglect of basic operations

Users of high-performance liquid chromatography (HPLC) often overlook mobile phase filtration (leading to chromatographic column blockage and a sharp rise in back pressure) or column temperature control (temperature fluctuations cause retention time drift of ±2%). A case showed that an uncalibrated intelligent pressure calibrator had a measurement deviation of ±20% due to sensor aging, far exceeding the allowable error range.

2.Lack of maintenance, accelerating equipment wear and tear

• Cleaning misunderstandings

Users of automatic bottle washers mix household detergents, which produce foam, leading to damage to the circulation pump and a 60% increase in maintenance costs.

• Lack of calibration

Intelligent radiation monitors that are not calibrated monthly with Cs-137 standard sources experience a 30% decrease in detector sensitivity, resulting in misjudgment of radiation levels.

3.Random parameter settings, reducing data credibility

Users of gas chromatography-mass spectrometry (GC-MS) often copy methods from literature without adjusting the gradient program according to instrument performance, leading to a 40% decrease in the resolution of target peaks. A laboratory missed radioactive contamination because it did not select the correct detection mode (γ-rays were mistakenly selected as α mode).

4.Weak awareness of safe operation, with prominent risk hidden dangers

Users of intelligent integrated distillation instruments failed to turn off cooling water according to regulations, causing distillate backflow and damaging the circuit; during the operation of freeze grinders, failure to wear anti-freeze gloves led to liquid nitrogen splashing and frostbite.

In the standardized use and maintenance of laboratory instruments, the basic operation of bottle washing cannot be ignored. A professional laboratory-specific bottle washer can contribute to the efficient conduct of experiments and equipment protection from the basic link.

The efficient application of intelligent instruments must be based on "standardized operation + systematic training". Laboratories should build a closed-loop system of "theoretical training - virtual simulation - practical assessment - continuous improvement", combined with ISO standards and digital tools, to control human error within 0.5%. In the future, AI-assisted diagnosis and digital twin technology will further promote the innovation of training models, helping laboratories transform from "experience-driven" to "data-driven".