01.
Detect
Using a quantum electrochemical sensor, we begin by looking closely at the sample through one of its most fundamental physical properties. We detect the vibrational frequency of the molecules and use it as a fingerprint.
MiniQES Bioanalytical Platform
Simplifying bioanalysis today; Futureproofing it for tomorrow
Why QES
Our bioanalytical platform enables Quantum Electrochemical Spectroscopy (QES). With a simple and universal workflow, QES translates a sample aliquot into a high-dimensional phenotypic signature, a digital twin, that can be re-analyzed and redefined in the cloud.
Simple
- No assay, no reagents
- No sample preparation
- 1 pipetting step, 30 minutes
Powerful
- Detects and quantifies
- Proteins, metabolites, cells.
- 2-4 ul of sample
Future Proof
- From physical sample to digital twin
- Analytes on-demand
- No additional sample consumption
MiniQES Bioanalytical Platform
Enabling scalable bioanalytical data collection without reagent, probes or sample prep.
With a small footprint, a very simple operation, minimal sample requirements, and powerful analytics, the MiniQES Platform enables the collection and processing of samples closer to the source without sacrificing content or robustness.
MiniQES Reader
- IoT Enabled device with the footprint of a 96-well plate that can be deployed as a standalone benchtop device or integrated with a liquid handler
QES Disposable Vial
- The sensor inside the disposable vial enables quantum electrochemistry for reliable data without the bias of reagents or complex sample preparation
Bioanalytical Cloud Services (BCS)
- AI-powered, simple, and an intuitive web-based interface to enable sample classification, biomarker discovery and analyte quantitation at high sensitivity and over a broad dynamic range
Versatile Bioanalytical Applications
From biomarker discovery to high multiplex quantification, QES technology can detect and quantify a broad range of signatures and analytes in a very short time:
Accelerate the discovery of disease pathways with broad-spectrum phenotypic signatures from proteins, metabolites, and cells in days and not months.
This AI-driven workflow is designed to quickly acquire data, find, validate, and test signatures that can reliably differentiate between groups of samples.
Instant experimental replication with new bioanalytical measurements stored in the cloud. No new sample aliquot is required.
QES produces a digital twin of your sample. This allows updates of the measured analytes as they are discovered or added to the database. Without using a new sample aliquot, you can update insights on-demand, saving precious samples, time in the lab, and money.
Drive efficiency in experimental models by using only 2-4 ul of sample and a reagent-free approach.
Using QES, you can produce a high-fidelity biochemical snapshot and customized assays for efficacy and toxicity simultaneously without needing specific reagents. Imagine the time and money you’ll save.
Assess purity and quality without the need for analyte-specific reagents.
Analyze batches without sample preparation and complex workflows, as QES allows you to detect and quantify your desired molecules quickly.
Let's Work Together
QESLab is a rolling collaboration grant program that funds projects for early use of the QES platform. We are exploring complementary and meaningful collaborations across diverse fields.
Join us as we garner new insights and accelerate the pace of biomedical and biological research
The QES Workflow
From biomarker discovery to high multiplex quantification, QES technology can detect and quantify a broad range of signatures and analytes in a very short time:
From a reagent-centric to a data-centric approach, in three simple steps
01.
Detect
Using a quantum electrochemical sensor, we begin by looking closely at the sample through one of its most fundamental physical properties. We detect the vibrational frequency of the molecules and use it as a fingerprint.
02.
Digitize
By changing the voltage bias, we acquire a high dimensional dataset in a wide range of frequences. This dataset is ten uploaded and becomes a digital twin of the sample.
02.
Digitize
By changing the voltage bias, we acquire a high dimensional dataset in a wide range of frequences. This dataset is ten uploaded and becomes a digital twin of the sample.
03.
Analyze
Find patterns and fingerprints on your cohorts of samples. Iterate between quantification of analytes and identification of signatures. Using the digital twin you can update analytes using updated libraries or reference data without having to re-run the sample.
03.
Analyze
Find patterns and fingerprints on your cohorts of samples. Iterate between quantification of analytes and identification of signatures. Using the digital twin you can update analytes using updated libraries or reference data without having to re-run the sample.
Isn't it time you moved from a reagent-centric to a data-centric approach?
Isn't it time you moved from a reagent-centric to a data-centric approach?
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