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QuanRecovery Vials and Plates with MaxPeak High Performance Surfaces (HPS)

Manuals | 2019 | WatersInstrumentation
Consumables
Industries
Manufacturer
Waters

Summary

Significance of the Topic


Advances in liquid chromatography–mass spectrometry (LC-MS) rely on minimizing sample losses during handling and storage. Non-specific binding (NSB) to container surfaces can degrade sensitivity and reproducibility, especially when analyzing low-abundance peptides and proteins. The development of specialized vials and plates with enhanced surfaces directly addresses these challenges, enabling more reliable quantification in proteomics and biomolecular research.

Objectives and Overview


This whitepaper introduces QuanRecovery Vials and Plates featuring MaxPeak High Performance Surfaces (HPS). Designed by LC-MS specialists, these consumables aim to:
  • Reduce analyte adsorption and NSB effects.
  • Enable low residual volumes for small-sample workflows.
  • Maintain compatibility with common autosampler formats.

The manual covers product specifications, usage guidelines for peptides, proteins, and other biomolecules, storage recommendations, and sealing options.

Methodology and Instrumentation


Material and Surface Treatment:
  • Base material: polypropylene.
  • Surface coating: proprietary MaxPeak HPS to block ionic and hydrophobic interactions.
  • Formats: 300 µL conical vials (ANSI-48 compatible) and 700 µL conical wells in 96-well plates (ANSI-96 format).

Key Specifications:
  • Residual volume: ≥5 µL in vials, ≥8 µL in plates at default needle depth.
  • pH compatibility: 0–14.
  • Max centrifugation (plates): 2,000 g.

Sealing Options:
  • Polypropylene cap mats, PTFE/silicone pre-slit mats, heat seals, adhesive seals.
  • Vial caps: PE septumless, PTFE/silicone pre-slit, black solid caps for storage.

Compatible Instrumentation:
  • Waters ACQUITY UPLC systems (I-Class, M-Class, H-Class, Arc, nanoACQUITY).
  • ACQUITY UPLC H-Class Bio, ACQUITY Arc Bio.
  • Other systems supporting ANSI-96 and ANSI-48 autosampler formats with appropriate dimension settings.


Key Results and Discussion


Performance evaluations demonstrate:
  • Significantly reduced peptide losses over 72 hours in autosamplers when compared to standard glass or polypropylene containers.
  • Enhanced protein stability under acidic storage conditions; neutral/basic pH requires case-by-case assessment.
  • Consistent low residual volumes that facilitate maximum injection volumes from limited sample quantities.

The uniform conical geometry and HPS treatment synergize to minimize surface-analyte interactions, yielding improved sensitivity and lower variability in quantitative assays.

Benefits and Practical Applications


Primary advantages include:
  • Higher recovery rates for peptides and proteins in LC-MS workflows.
  • Improved reproducibility for biomolecular quantification.
  • Reduced sample consumption and waste.
  • Broad pH range compatibility suitable for diverse assay conditions.

Applications span targeted proteomics, peptide bioanalysis, affinity-purified samples, and other biomolecule-centric assays where NSB is a limiting factor.

Future Trends and Opportunities


Emerging directions include:
  • Extension of HPS chemistry to microfluidic and nano-LC platforms.
  • Integration with automated sample-preparation robots to streamline workflows.
  • Development of bespoke surface treatments tailored to specific biomolecule classes.
  • Combining HPS with blocking agents or carrier proteins for extreme low-concentration assays.

These innovations will further enhance sensitivity and robustness in next-generation bioanalytical methods.

Conclusion


QuanRecovery Vials and Plates with MaxPeak High Performance Surfaces represent a targeted solution to NSB challenges in LC-MS biomolecule analysis. By delivering low residual volumes, wide pH compatibility, and autosampler readiness, they enable researchers to achieve higher sensitivity and reproducibility in peptide and protein quantification.

References


No external literature cited in source document.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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