{
  "describedBy": "https://emanahmed.org/openapi.json",
  "type": "research-overview",
  "author": {
    "name": "Eman Ahmed",
    "orcid": "https://orcid.org/0009-0008-7822-9530",
    "affiliation": "Rutgers, The State University of New Jersey",
    "url": "https://emanahmed.org/"
  },
  "areas": [
    {
      "id": "protein-stabilization",
      "type": "research-area",
      "title": "Polymer-stabilized enzymes in organic solvents",
      "period": "Doctoral project, Gormley Lab, 2022-2026",
      "summary": "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.",
      "tags": [
        "Random copolymers",
        "Polymer-enzyme hybrids",
        "Biocatalysis",
        "Solubility assays",
        "Automation"
      ],
      "url": "https://emanahmed.org/research.html#protein-stabilization",
      "deepDiveUrl": "https://emanahmed.org/research/polymer-stabilized-enzymes.html"
    },
    {
      "id": "machine-learning",
      "type": "research-area",
      "title": "Machine learning for biomaterial structure-function mapping",
      "period": "First-author review, Tissue Engineering Part A, 2024",
      "summary": "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.",
      "tags": [
        "Structure-property relationships",
        "Data mining",
        "Model selection",
        "Biomaterials discovery"
      ],
      "url": "https://emanahmed.org/research.html#machine-learning",
      "deepDiveUrl": "https://emanahmed.org/research/machine-learning-biomaterials.html"
    },
    {
      "id": "automation",
      "type": "research-area",
      "title": "Automated polymer synthesis and analysis",
      "period": "photo-ATRP platform and SAXS Assistant",
      "summary": "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.",
      "tags": [
        "photo-ATRP",
        "Liquid handling robotics",
        "SAXS",
        "Open-source tooling",
        "Reproducibility"
      ],
      "url": "https://emanahmed.org/research.html#automation",
      "deepDiveUrl": "https://emanahmed.org/research/automated-photo-atrp.html"
    }
  ],
  "methods": [
    {
      "area": "Polymer chemistry",
      "detail": "Photoinduced ATRP, PET-RAFT, random copolymer synthesis, vacuum filtration, solubility characterisation"
    },
    {
      "area": "High-throughput experimentation",
      "detail": "Liquid handling robotics, 96-well plate assay design, automated enzyme activity and solubility screening"
    },
    {
      "area": "Structural characterisation",
      "detail": "Small-angle X-ray scattering, Guinier and P(r) analysis, Kratky interpretation, synchrotron data workflows"
    },
    {
      "area": "Computation",
      "detail": "Python, scikit-learn, multilayer perceptron and Gaussian mixture models, data visualisation, experimental design automation"
    },
    {
      "area": "Biophysics",
      "detail": "Protein stabilization, biocatalysis in water-miscible organic solvents, coarse-grained modelling of lipid membranes"
    }
  ],
  "expertise": [
    "High-throughput experimentation",
    "Machine learning for biomaterials",
    "Atom transfer radical polymerization (ATRP)",
    "Photoinduced ATRP",
    "Reversible-deactivation radical polymerization",
    "Random copolymers",
    "Protein stabilization",
    "Polymer-enzyme hybrids",
    "Biocatalysis in organic solvents",
    "Small-angle X-ray scattering (SAXS)",
    "Laboratory automation",
    "Liquid handling robotics",
    "Structure-property relationships",
    "Polymer chemistry",
    "Biomaterials discovery"
  ],
  "license": {
    "content": "CC-BY-4.0",
    "contentUrl": "https://creativecommons.org/licenses/by/4.0/",
    "note": "Site prose and this API's metadata may be reused with attribution to Eman Ahmed (emanahmed.org). Published abstracts are reproduced from the publisher of record named in each record's `doi`; their reuse is governed by that publisher, not by this licence.",
    "attribution": "Eman Ahmed, https://emanahmed.org"
  }
}