What if the electronics we currently use for discovery and research could work with biology instead of just next to it? In this blog post, our Co-Founder and CTO, Brett Goldsmith, will share the exciting news of our new company, Cardea, and the “Internet of Biology” revolution.
In this blog post, we take a look at the differences between these types of transistors and explain what they do.
To generate sensitive kinetic binding results, Agile biosensors need graphene surfaces with zero defects. How do we guarantee perfect biosensors with such a delicate, breakthrough material? Take a peek at the blog post for more details!
We’ve launched a new biosensor surface chemistry! The new SCOOH sensor drastically reduces nonspecific hydrophobic binding with 25 times the binding sites of our existing COOH sensor. It’s designed to measure weak binders in high concentrations, for a quick verification of positive hits from primary screens. Learn how the SCOOH sensor is created in our blog post.
Innovative cell- and gene-based therapies have been entering the market recently, but their success hinges on the monitoring of critical biomarkers to achieve desired therapeutic effects. A new publication in Biosensors and Bioelectronics suggests that graphene-based biosensors can demonstrate “high sensitivity for a broad range of target molecule sizes [with] multiplex assay potential and reduced manufacturing costs compared to optical…approaches,” and they give Agile R100 a special call-out! See our blog post for more details.
With the weather warming up, we wonder: How do we avoid mosquito bites with the dangerous diseases that they potentially carry, including the Zika virus? Recently a paper published for the International Conference on Digital Health proposed using a modified clinical version of Agile R100 as a portable graphene biosensor that can provide prompt data to be fed into robust forecasting to help stop the spread of the Zika virus. Read our blog post for more details and a link to the paper!
Vanderbilt University scientists noticed an amazing aspect of graphene in a recent publication in Nature Communications: Nerve cell membranes pulled in more cholesterol when grown on graphene than when grown on glass alone. Why is this important? Because cholesterol is a neurotransmitter, and more neurotransmitter means stronger signals and more neuronal firing between cells, which could greatly impact neurological diseases. See our blog post for more details!
What if you didn’t have to run your experiment in a cold room, but could instead bring the cold room to your assay platform… and stay perfectly comfortable doing so? Agile R100 can be used on a bed of ice to maintain the stability of your protein! See our blog post for more details on our GPCR characterization experiment, performed on a bed of ice.
Ahoy, Mateys! We had our company Halloween party last week, pirate style. Hosted at the house of one of our scientists, it was complete with a handmade shipwreck. Read more details about our company personality in our blog post!
We have a shiny new toy – our new AI Analyzer by Nanotronics! It’s the first QA tool ever used to quality check mass-manufactured graphene biosensors, and it’s making our QA process go 20 times faster than it would manually. Read more details in our blog post!
As a scientist, you have preferred, go-to methods for testing molecular interactions, such as surface plasmon resonance (SPR), Bio-Layer Interferometry (BLI), ELISA, or fluorescence labeling. So, what is the best method to use if you’re measuring a small molecule therapeutic drug interacting with a targeted protein?
One way of characterizing a system is to consider only its inputs and outputs with no concern for its inner workings. This opaque system is called a black box, and black boxes are useful shortcuts when you need fast answers. However, when your input doesn’t give you the correct output, then what do you do? As a scientist, knowing how the black box works and how to modify it can help you have confidence in the results.
Scientists have been dealing with biomolecules for decades, and several strategies have been developed to reduce background signals that contribute to poor assay performance. Even better, new technologies are being released to market that make the most finicky aspects of blocking a thing of the past.
Reference samples can consume sensor cells and precious biomaterial, and any difference between the reference run and experiment run adds inaccuracy to the data. Isn’t getting accurate data hard enough already? Reduce or eliminate bulk responses from solvent effects in your measurements!