MOTIVATION LETTER The Conjunctive Consolidation Threshold model, validated through Bayesian MCMC simulation on ODE/RK45 dynamics, reduces encoding probability from 0.855 to 0.122, an 85.8 percent reduction with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. The CCT model specifies a tripartite pharmacological framework for preventing reward-memory encoding in addiction. The open-source platforms built around it, IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions, and GATE for BCI neural-stimulation safety evaluation, are public goods available on GitHub under Apache 2.0. The Web3 grant ecosystem, as catalogued by The Signal Directory, funds precisely this kind of open-source, pre-registered, computationally validated research infrastructure. My provisional patent on the CCT core architecture, filed Q3 2026, protects the mechanism while the code remains freely accessible. Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the neuroscientific foundation. The Bayesian population dynamics paper on Zenodo (DOI 10.5281/zenodo.20492472) and the formal mathematical specification on OSF (DOI 10.17605/OSF.IO/EMY4U) provide the full technical record. I am an independent researcher based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and a German equivalent grade of 1.9. I am not yet enrolled in an MSc programme; I am applying for October 2026 start at MUG/Graz, Austria. This grant would fund the extension of IMPRINT into a blockchain-verifiable screening tool, allowing decentralized clinical trial data collection across African populations where addiction liability data is scarce. The Web3 focus on public goods, retroactive funding, and demonstrable impact aligns with my existing workflow: pre-registered hypotheses, open-source code, and peer-reviewed preprints. I am applying to the programmes listed in The Signal Directory’s 2026 Web3 Grant Programs list, specifically those prioritizing open-source research tools and LMIC-track applicants. RESEARCH STATEMENT My independent research programme centers on the Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for preventing reward-memory encoding in addiction. The model was developed and validated entirely outside a formal academic position, using ODE/RK45 numerical integration and Bayesian MCMC methods implemented in Python with scipy, numpy, and PyMC. The foundational preprint on OSF (DOI 10.17605/OSF.IO/KG7B5) establishes the neurobiological rationale: dopamine, glutamate, and norepinephrine signals must conjunctively cross a threshold for memory consolidation to occur. The formal mathematical specification (OSF DOI 10.17605/OSF.IO/EMY4U) derives the differential equations governing the three-signal interaction. The Bayesian population dynamics paper (Zenodo DOI 10.5281/zenodo.20492472) simulates a clinical trial architecture with 1,000 virtual subjects, confirming that the CCT intervention reduces encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points over any single-target approach. All five pre-registered hypotheses were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper on alcohol addiction mechanisms is under review at Alcohol (Elsevier). The three open-source platforms, IMPRINT (addiction-liability screening), TOPOLOGIX (topological data analysis for drug-protein interactions using persistent homology and bipartite simplicial complexes, with a hERG cardiotoxicity MVP), and GATE (BCI neural-stimulation safety evaluation, Apache 2.0), are all publicly available on GitHub. The provisional patent on the CCT core architecture, filed Q3 2026, protects the mechanism while the code remains open. For Web3 grant programmes, the key deliverable is a blockchain-verifiable version of IMPRINT that records screening results on-chain, enabling decentralized clinical trial data collection across African populations. The Bayesian validation framework and pre-registered hypothesis structure provide the reproducibility guarantees that Web3 funders require. The endorsements from Berridge, Gershman, Daw, and Mattar confirm the theoretical soundness. The technical stack, Python, R, TDA with Ripser and Gudhi, NEURON and Brian2 for neural simulation, AlphaFold and RDKit for drug-protein interaction prediction, ADMET and QSAR for toxicity screening, GROMACS and AutoDock for molecular dynamics, Nextflow and SLURM for HPC workflows, Supabase and Postgres for data management, JavaScript and Node.js for front-end deployment, covers the full pipeline from molecular simulation to clinical trial simulation to user-facing tool. The next step is to integrate IMPRINT with a blockchain oracle for tamper-proof trial data, then deploy a pilot study across three Nigerian clinical sites. SHORT ESSAY: ECOSYSTEM IMPACT The CCT model and its associated platforms address a specific gap in the Web3 ecosystem: the lack of verifiable, open-source tools for addiction liability screening that can operate across decentralized clinical trial networks. IMPRINT currently screens compounds for addiction liability using the CCT framework. By deploying IMPRINT on-chain, each screening result becomes a verifiable record, enabling transparent, auditable clinical trial data collection. This is directly relevant to Web3 grant programmes that fund public goods and developer tools. The Bayesian validation framework, all five pre-registered hypotheses confirmed, 85.8 percent reduction in encoding probability, provides the quantitative impact metric that retroactive funding mechanisms require. The open-source code on GitHub, the preprints on OSF and Zenodo, and the Apache 2.0 license on GATE all satisfy the public goods criterion. The LMIC angle is specific: addiction liability data from African populations is nearly nonexistent in the literature. A blockchain-verifiable screening tool deployed in Nigeria would generate novel, high-quality data while demonstrating the Web3 model for decentralized research. The provisional patent protects the core mechanism without restricting access to the code, balancing IP protection with open-source ethos. The endorsements from four leading computational neuroscientists provide external validation that the science is sound. The technical infrastructure, Python, PyMC, ODE solvers, TDA, neural simulation, molecular docking, is already built and tested. The grant would fund the blockchain integration layer, the pilot deployment, and the data analysis pipeline. CHECKLIST - [ ] Motivation letter (300-500 words, written above) - [ ] Research statement (400-600 words, written above) - [ ] Short essay on ecosystem impact (200-350 words, written above) - [ ] CV or resume with ORCID, GitHub, and publication links - [ ] Links to three preprints on OSF and Zenodo with DOIs - [ ] Links to GitHub repositories for IMPRINT, TOPOLOGIX, and GATE - [ ] Provisional patent filing documentation (Q3 2026) - [ ] Endorsement letters or contact information for Berridge, Gershman, Daw, Mattar - [ ] Proof of PCN pharmacist license - [ ] B.Pharm transcript and degree certificate - [ ] Proof of independent researcher status (no current university affiliation) - [ ] Budget proposal for blockchain integration and Nigerian pilot deployment - [ ] Timeline for October 2026 MSc application at MUG/Graz, Austria - [ ] Completed application form on The Signal Directory or individual grant programme portals EDITOR NOTES - Eligibility risk: Some Web3 grant programmes require a registered entity (LLC, foundation, DAO). Eniola is an independent researcher. Check if a Nigerian business registration or a fiscal sponsor is needed. - Verification needed: Confirm that the provisional patent filing date (Q3 2026) is accurate and that the patent does not conflict with open-source licensing on GitHub. - Gap to fill: The profile does not specify which specific Web3 grant programmes from The Signal Directory list are being targeted. Eniola should select 3-5 programmes (e.g., Ethereum ESP, Optimism RPGF, Arbitrum RFP, Solana Foundation, Base) and tailor the application to each one’s specific criteria and deadlines. - Personal detail needed: The profile does not include a personal story or motivation for working on addiction in the Nigerian context. Adding a brief, specific anecdote about a clinical case or a public health statistic from Nigeria would strengthen the LMIC angle. - Budget detail needed: The application materials do not include a specific funding amount or budget breakdown. Eniola should prepare a line-item budget for blockchain integration, pilot deployment, and data analysis, with a total request between $10,000 and $50,000.