Prisma Bioinnovations

DIVERGENT Scientific Intelligence platform! Built a working prototype that uses OpenAI and Grok to independently analyze scientific literature, highlight disagreements and evidence gaps, and support human-directed research decisions.

From Plastic Surface Chemistry to the Z-Model: Surface Area, Interfacial Reactivity, and the Evolution of a Cross-Domain Research Framework (2024–2026) doi.org/10.5281/zenodo.23425…

New PCT filed: System and Methods for Generating Reproducible Digital Twins For lab assays and extending to manufacturing outputs for many other industries. 1/2

New Technical Paper which demonstrates Spatiotextural Approach is not limited to microplastics / nanoplastics. Assay Design for Computer Vision: From Chemical Reactions to Spatiotextural Measurement in Water and Other Liquids doi.org/10.5281/zenodo.22729…

Introducing the Spatiotextural Computer Vision Approach to Optical Assays The framework analyzes assay-generated spatial patterns, texture, gradients, and intensity distributions, even when individual particles cannot be directly seen or counted. doi.org/10.5281/zenodo.22721…

PCT application filed describing Adaptive Matrix Conditioning: a generalized framework for improving analytical performance by first defining desired analytical performance, determining matrix / liquid constraints, and applying targeted conditioning prior to analysis.

In particulate systems, under-dosed reagent may underestimate concentration due to incomplete Z-site occupancy. Correct or over-dosed? All Z-sites are occupied excess reagent simply settles while central optical analysis stays accurate. Spontaneous observation.

New Technical Paper: The Z-Model: A Unified Accessible Surface Area Framework for Predicting Particulate Behavior Across Environmental, Biological, and Material Systems doi.org/10.5281/zenodo.19659… 1/2

Most lab protocols still rely on fixed times: 60 min. Overnight. But even simple factors like temp matter. Reactions run faster when warm, slower when cold. yet protocols rarely adjust. Our Adaptive Endpoint framework is based on system kinetics, not just a ⏰. NP submitted.

Traditional optics reduces complex systems to single numbers (e.g. turbidity or color) at 1 time point. Real materials: liquids, gels, aerosols, aren’t scalar. Our Generalized Optical Metrology framework measures how optical fields evolves over time. Nonprovisional submitted

Happy New Year! 🥳✨💫 The first patent we developed is something deceptively simple: turning physical interactions in fluids into a reproducible optical score. Before AI. Before models. Measurement comes first because everything downstream inherits it. 🩵

Whether particles are in solution or dry form, the challenge is the same: extracting stable, comparable physical signals from real images. We builds the optical + AI metrology layer that makes this possible and enables digital twins grounded in measurement, not simulation.

We use physics-aware AI to quantify particle size distributions, morphology, surface texture,and optical response in dry and semi-dry systems. These methods support reproducible measurement across materials, biological powders, and environmental samples.

Optical metrology for particles in solution We develop physics-aware AI methods using photos to quantify particle behavior in fluids including distribution, aggregation, edge dynamics, and optical signal evolution grounded in real imaging data.

AI Digital Twins for lab assays are a core focus. We build physics-aware digital replicas of experimental workflows to improve assay reproducibility, signal interpretation, and cross-lab comparability. All grounded in real optical and experimental data.

“Computer vision” as typically defined, object detection, isn’t enough for science. Prisma Bioinnovations focuses on AI and optical metrology, not just seeing patterns, but extracting validated physical signals from images. Measurement > classification. Reproducibility > demos.

Gotta try all these BARKS from @BHG 🎄🍫🤤 Which one do you like the most? Even more in the article. Link in reply. 😎