Eman AhmedRutgers BME

Eman Ahmed, PhD candidate, Rutgers Biomedical Engineering Ninety-six polymers at a time.

I build robot-run polymer chemistry and the machine learning that reads what comes back, to find the polymers that keep enzymes working in solvents where they would normally fall apart.

Read the research Publications Google Scholar
Three linked panels. A 96-well plate of polymer reactions, a solution-scattering detector image, and a scattering curve with a small neural network, joined by a return arrow back to the plate.Synthesise96 polymer reactions, one plateMeasuresolution scattering, ring by ringModelfit the curve, learn the rulepredicted structurethe next ninety-six are chosen by the model
Three linked panels. A 96-well plate of polymer reactions, a solution-scattering detector image, and a scattering curve with a small neural network, joined by a return arrow back to the plate.Synthesise96 polymer reactions, one plateMeasuresolution scattering, ring by ringModelfit the curve, learn the rulepredicted structurethe next ninety-six are chosen by the model

One turn of the loop. Ninety-six polymer reactions run in parallel on a single plate; each product is measured by solution scattering; a model reads the curves and picks what goes on the next plate. The rings are the real form-factor minima of a 9 nm particle, computed from scattering physics rather than drawn: the calculation is on the SAXS page.

3peer-reviewed papers, one as first author
43citations
3h-index

Metrics from Google Scholar, September 2026.

What I work on

Three connected problems: making enzymes survive outside water, generating enough polymer data to learn from, and building analysis that keeps pace with the synthesis.

Polymer-stabilized enzymes in organic solvents

Doctoral project, Gormley Lab, 2022-2026

Enzymes are extraordinary catalysts in water and frequently useless outside it. Move one into a water-miscible organic solvent, which is often where the interesting synthetic chemistry happens, and it tends to unfold, aggregate and drop out of solution.

Random copolymers can act as synthetic chaperones, wrapping a protein in a shell whose chemistry can be tuned monomer by monomer. The difficulty is that the design space is enormous and the structure-function relationship is not obvious from first principles. My work builds plate-based, automated assays that measure solubility and retained activity across large copolymer libraries, so the search can be run empirically at scale rather than one rational design at a time.

  • Random copolymers
  • Polymer-enzyme hybrids
  • Biocatalysis
  • Solubility assays
  • Automation

In depth, with the data

1122.5k2.1Mmadepossible

Machine learning for biomaterial structure-function mapping

First-author review, Tissue Engineering Part A, 2024

High-throughput experiments produce the kind of data that models need: many conditions, consistent measurement, and, importantly, retained failures. The question is what to do with it.

My review in Tissue Engineering Part A surveys how machine learning is being used to identify the physicochemical cues that govern biomaterial performance, across tissue engineering, gene delivery, drug delivery, protein stabilization and antifouling surfaces, along with the data-mining approaches that let published results stand in for experiments that have not been run yet.

  • Structure-property relationships
  • Data mining
  • Model selection
  • Biomaterials discovery

In depth, with the data

Automated polymer synthesis and analysis

photo-ATRP platform and SAXS Assistant

Oxygen-tolerant reversible-deactivation radical polymerization made it possible to run controlled polymer chemistry in open labware. That is what puts it within reach of a liquid handler, and it is the premise behind our automated photo-ATRP platform, which screens ligands and initiators for monomers, methyl methacrylate among them, that are slow enough to be painful to optimise by hand.

Synthesis throughput is only useful if characterisation keeps up. SAXS Assistant addresses the other end of the pipeline: it automates small-angle X-ray scattering analysis, estimates maximum particle dimension with a model trained on 1,940 experimental profiles, and refuses to report results it cannot stand behind.

  • photo-ATRP
  • Liquid handling robotics
  • SAXS
  • Open-source tooling
  • Reproducibility

In depth, with the data

Why any of this needs a robot

Not because throughput is impressive. Because the design space is large enough that choosing what to make is the actual problem, and choosing well needs data that includes the failures.

What gets made, against what could be made

Study sizes from the biomaterials literature, on a logarithmic axis. The largest hand-built polymer libraries stop a thousandfold short of the spaces they are sampling.

Dot plot on a log axis, from 112 polymers to 2.1 million possible drug-excipient pairings.10²10³10⁴10⁵10⁶10⁷Kohn polyacrylate library: 112Kohn polyacrylate library112 distinct degradable polymers112Scaffold study: 182Scaffold study13 polymers, 182 scaffolds tested182Titanium nanotube analysis: 272Titanium nanotube analysis272 labelled samples, 30 publications272Lipid nanoparticle screen: 1,080Lipid nanoparticle screen1,080-LNP formulation library1,080Poly(β-amino ester) library: 2,500Poly(β-amino ester) library~2,500 degradable PAEs for gene delivery2,500Drug–excipient space: 2,100,000Drug–excipient space2.1 million possible pairings2,100,000Number of distinct formulations or samples (log scale)
Show the numbers
StudySizeDescription
Kohn polyacrylate library112112 distinct degradable polymers
Scaffold study18213 polymers, 182 scaffolds tested
Titanium nanotube analysis272272 labelled samples, 30 publications
Lipid nanoparticle screen1,0801,080-LNP formulation library
Poly(β-amino ester) library2,500~2,500 degradable PAEs for gene delivery
Drug–excipient space2,100,0002.1 million possible pairings

Reported Study sizes as cited in Ahmed et al., Tissue Engineering Part A 30(19–20), 662–680 (2024). The full argument, with the methods.

In depth

Four longer pieces with the figures: what each method does, the numbers behind it, and where it stops working.

Publications

Three peer-reviewed papers in Tissue Engineering Part A, ACS Polymers Au and Biophysical Journal, plus the doctoral manuscript in preparation.

First authorIn preparation

High-Throughput Approach for Evaluating the Solubility of Polymer-Stabilized Proteins in Organic Solvent

Eman Ahmed, Adam J. Gormley

Manuscript in preparation · Gormley Lab, Rutgers University

All publications, with summaries and citation formats

Research notes

Plain-language companions to the papers: what the method does, why it was built that way, and what it does not solve.

What oxygen tolerance buys you in polymer chemistry

Controlled radical polymerisation used to require sealed, degassed glassware. Oxygen-tolerant chemistry moved it into open well plates, and that one change is what puts polymer synthesis within reach of a liquid-handling robot.

Trusting a SAXS analysis you didn't do by hand

Small-angle X-ray scattering analysis is full of judgement calls that vary between analysts. Automating it is less about speed than about making those calls explicit, and about flagging the profiles where the answer should not be trusted.

All notes

Working on something adjacent?

If you work on high-throughput polymer synthesis, machine learning for materials, SAXS analysis or enzyme stabilization, I would like to hear about it, whether that is a collaboration, a dataset worth combining, or a question about how one of these methods behaves in practice.

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