{
  "sample_id": "naufal_tsabitul_azmi_2026-10-05_sfgfp_mg76_v1",
  "sample_type": "experimental",
  "reagent_list": {
    "amino_acid_mix_17": 1600,
    "amp": 600,
    "base_buffer": 2000,
    "cmp": 425,
    "gmp": 425,
    "hepes_koh": 900,
    "kpo_monobasic_mix": 600,
    "lysate": 5000,
    "magnesium_glutamate": 200,
    "nicotinamide": 800,
    "nuclease_free_water": 200,
    "potassium_glutamate": 3325,
    "ribose": 1500,
    "template_sfgfp": 2000,
    "ump": 425
  },
  "metadata": {
    "ai_model": "Codex desktop",
    "protein_target": "sfGFP",
    "acknowledgements": "Ginkgo sfGFP Ribose reference sfGFP_R2PC_054; Jewett & Swartz (2004), DOI 10.1002/bit.20026; Olsen et al. (2026), DOI 10.1038/s41467-026-69605-8.",
    "comments": "HW3 initial hypothesis: reduce added magnesium_glutamate from 225 to 200 nL (one 25-nL step), giving nominal Mg(Glu)2 7.6 mM including 2.6 mM from fixed base buffer, with water increased from 175 to 200 nL. Keep the other sfGFP Ribose additives fixed. Test whether tuning magnesium improves folded sfGFP output and hence cost per functional protein. No yield prediction or experimental result is claimed; compare to sfGFP_R2PC_054 under the same assay conditions. AI client and actual prompts are recorded in the homework journal."
  }
}