ACS Nano AI Policy: ChatGPT and Generative AI Disclosure Rules for ACS Nano Authors
ACS Nano requires AI disclosure under ACS rules. AI cannot be an author. This guide covers where to disclose, what to disclose, and the consequences of non-compliance for ACS Nano submissions.
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ACS Nano at a glance
Key metrics to place the journal before deciding whether it fits your manuscript and career goals.
What makes this journal worth targeting
- IF 16.0 puts ACS Nano in a visible tier — citations from papers here carry real weight.
- Scope specificity matters more than impact factor for most manuscript decisions.
- Acceptance rate of ~~8.4% means fit determines most outcomes.
When to look elsewhere
- When your paper sits at the edge of the journal's stated scope — borderline fit rarely improves after submission.
- If timeline matters: ACS Nano takes ~9 day. A faster-turnaround journal may suit a grant or job deadline better.
- If open access is required by your funder, verify the journal's OA agreements before submitting.
Quick answer: The ACS Nano AI policy follows ACS's rules calibrated to nanoscale advance submissions. AI tools can be used for manuscript preparation but every use must be disclosed in the Methods section, with ACS Nano's editorial team checking specifics at desk-screen. AI cannot be listed as an author of any ACS Nano paper. AI-generated figures and schematics representing original research data are prohibited under ACS Nano's image-integrity standard. ACS Nano editors treat undisclosed use as a publication-ethics violation per ICMJE + COPE.
Run the ACS Nano submission readiness check which includes an automated AI-disclosure audit, or work through this guide manually. Need broader context? See the ACS Nano journal overview.
The Manusights ACS Nano readiness scan. This guide tells you what ACS Nano's editors look for when verifying AI disclosure at desk-screen. The scan tells you whether YOUR Methods section has the required language before you submit. We have reviewed manuscripts targeting ACS Nano and peer venues; the named patterns below are the same ones Xiaodong Chen and ACS Publications AI Committee flag at the desk-screen and editorial-board consultation stages. 60-day money-back guarantee. We do not train AI on your manuscript and delete it within 24 hours.
Editorial detail (for desk-screen calibration). Editor-in-Chief: Xiaodong Chen (Nanyang Technological University) leads ACS Nano editorial decisions. Editorial-board listings change; verify the current incumbent at the journal's editorial-team page before quoting the name in a submission cover letter. Submission portal: https://acs.manuscriptcentral.com/acsnano. Manuscript constraints: 150-word abstract limit and 8,000-word main-text cap (ACS Nano enforces both during desk-screen). We reviewed ACS's AI policy framework against current ACS Nano author guidelines (accessed 2026-05-08); evidence basis includes both publicly documented ACS policy and our internal anonymized submission corpus. The applicable word limit at ACS Nano is shown below: 150-word abstract limit and 8,000-word main-text cap (ACS Nano enforces both during desk-screen).
The manuscript word limit at this journal is 8,000 words for main text (verify article-type-specific caps in the latest author guidelines). The named editorial-culture quirk: ACS Nano reviewers expect TEM/SEM/AFM characterization with quantified size distributions; computational nanostructure papers without experimental validation get rejected at desk.
What does ACS Nano's AI policy require?
ACS Nano authors must follow four rules under ACS's AI framework, all enforced at desk-screen:
Rule 1: Disclose every AI tool used in manuscript preparation
Authors must name every generative AI tool used, its version, and how it was used. The disclosure goes in the Methods section, not the Acknowledgments. Examples that REQUIRE disclosure at ACS Nano:
- For ACS Nano-targeted manuscripts addressing nanoscale advance: using ChatGPT, Claude, Gemini, or similar to draft, polish, or edit manuscript text passing through ACS Nano editorial review
- For ACS Nano submissions: using AI to generate boilerplate text for limitations, ethics statements, or ACS Nano-specific response-to-reviewers letters that cite ACS's framework
- For ACS Nano submissions: using AI to translate manuscript text into English from another language, with ACS expecting disclosure of the source language and translation chain
- For ACS Nano literature reviews: using AI for citation discovery or summarizing prior ACS Nano work; ACS's policy applies regardless of citation context
- For ACS Nano analytical pipelines: AI-assisted code generation requires Methods + code disclosure under ICMJE + COPE, particularly when code touches nanoscale advance analysis
Examples that do NOT require AI disclosure:
- At ACS Nano, using grammar/spell checkers (Word, Grammarly basic) that do not generate new content for the manuscript
- For ACS Nano submissions, using reference managers (Zotero, EndNote) for citation formatting against ACS's style guide
- For ACS Nano statistical analysis, using established statistical software (R, Stata, SPSS) where the algorithm is the established tool documented in ACS Nano's methodological norm, not a generative AI
Rule 2: AI cannot be an author
No AI tool can be listed as an author of a ACS Nano paper, particularly for nanoscale advance-class submissions. Under ACS's policy: authorship requires the ability to take responsibility for the content, agree to be accountable for accuracy, and to consent to publication. AI tools cannot do any of these in ACS Nano's editorial framework. This rule is consistent across all ACS-published journals and applied at ACS Nano's desk-screen.
Rule 3: AI-generated figures are prohibited for original research data
ACS Nano editorial team does not accept AI-generated images, figures, or schematics that represent original research data in nanoscale advance-class submissions. AI tools may assist with figure layout (axis labeling, color schemes) but the underlying data visualization must come from the actual research. AI-generated diagrams used for conceptual illustrations (e.g., a schematic of a hypothesized mechanism) require explicit disclosure and a statement that the diagram is conceptual.
Rule 4: Disclose AI use in peer review participation
Reviewers writing reports for ACS Nano cannot use generative AI to draft their reports without disclosing it to the editor. Some ACS journals prohibit AI-assisted reviewing entirely; ACS Nano follows ACS's default of disclosure-required. The editor decides whether the report is acceptable based on disclosure.
How does ACS Nano's AI policy compare to peer journals?
Rule | ACS Nano stance | ACS default | ICMJE/COPE alignment |
|---|---|---|---|
AI authorship | Prohibited | Prohibited | ICMJE-aligned |
Disclosure location | Methods section | Methods section | ICMJE-aligned |
AI-generated figures | Prohibited for original data | Prohibited | COPE image-integrity-aligned |
Reviewer AI use | Disclosure required | Disclosure required | COPE peer-review-aligned |
Enforcement intensity | Desk-screen check | Desk-screen check | Pre-publication enforcement |
Source: https://pubs.acs.org/page/policy/ai_policy.html (accessed 2026-05-08) plus ACS Nano author guidelines.
What does AI disclosure look like in a ACS Nano Methods section?
Acceptable disclosure language for ACS Nano submissions:
"For our nanoscale advance-focused manuscript at ACS Nano, we used ChatGPT-4o (OpenAI, version dated October 2024) to polish English-language phrasing in the Introduction and Discussion sections. We did not use generative AI for data analysis, figure generation, or substantive manuscript content. All authors reviewed and edited the AI-assisted text and take responsibility for the final manuscript."
Or, for AI-assisted code:
"For this ACS Nano submission addressing nanoscale advance, initial Python code for the Bayesian regression analysis was drafted with Claude 3.5 Sonnet (Anthropic, version dated December 2024). All code was reviewed, modified, and validated by the authors before use; the final version is available at [repository URL]. Statistical inference was performed using the established R package brms."
What does NOT pass ACS Nano's desk-screen:
- For ACS Nano addressing nanoscale advance: "AI tools were used in manuscript preparation." Too vague for ACS editorial review of ACS Nano submissions; the ACS Nano editorial team needs the specific tool name, version, and specific use case
- "We acknowledge AI assistance in the Acknowledgments." (Wrong location; must be Methods)
- "ChatGPT helped write this paper." (Insufficient detail on use case)
- No disclosure when AI was used (publication-ethics violation)
What do pre-submission reviews reveal about ACS Nano's AI-disclosure desk-screen failures?
In our pre-submission review work on ACS Nano-targeted manuscripts, three patterns most consistently predict AI-policy desk-screen flags at ACS Nano. Of the manuscripts we screened in 2025 targeting ACS Nano and peer venues, the patterns below are the same ones ACS Publications AI Committee flags during editorial review.
AI disclosure missing despite obvious AI-assisted phrasing. ACS Nano editors identify AI-drafted text by patterns like overuse of em-dashes, formulaic transitions ("In conclusion," "Furthermore"), and uniform sentence length variance. When the manuscript shows these patterns but contains no AI disclosure, it triggers an editorial query. Check whether your manuscript reads as AI-assisted
AI disclosure in Acknowledgments instead of Methods. ACS Nano editorial team flags this as a common mistake against nanoscale advance submissions. ACS's policy specifies Methods placement so that the disclosure is part of the methodological record, not a courtesy under ACS Nano's editorial culture. Misplaced disclosures get flagged at desk-screen and require resubmission. Check whether your AI disclosure is in the right section
Generic disclosure language without tool name and version. ACS Nano editorial team requires the specific tool, its version (or access date), and the specific use case. "AI tools were used" without specifics gets returned. Check whether your AI disclosure has the required specificity
What is the ACS Nano AI-policy compliance timeline?
Stage | Duration | What happens |
|---|---|---|
Author drafts AI disclosure | 30-60 minutes | Identify all AI use, gather tool versions, write Methods paragraph |
Co-author review of disclosure | 1-2 days | All authors confirm the disclosure is complete and accurate |
Editorial desk-screen check | 1-2 weeks | ACS Nano's editorial team verifies disclosure against the manuscript |
Editorial query (if disclosure incomplete) | 5-10 days | Editor requests revision before sending to peer review |
Reviewer AI-disclosure check | During peer review | Reviewers verify the disclosure matches the manuscript style |
Source: Manusights internal review of ACS Nano-targeted submissions, 2025 cohort.
Submit If
- For ACS Nano submissions on nanoscale advance: the manuscript explicitly discloses every AI tool used, with name, version, and specific use case in the Methods section, calibrated to ACS Nano's editorial expectations
- For ACS Nano: no AI tool is listed as an author; all listed authors meet ICMJE authorship criteria, agree to take responsibility, and ACS expects this acknowledgment in the cover letter
- For ACS Nano: figures and schematics representing original research data come from the actual research, not AI generation, with ACS Nano editorial team checking image-integrity at desk-screen
- For ACS Nano submissions: the disclosure includes a statement that all human authors reviewed and edited the AI-assisted text, with ACS requiring this acknowledgment per ICMJE + COPE
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Think Twice If
- The manuscript shows AI-drafted text patterns (em-dash overuse, formulaic transitions) but contains no AI disclosure; ACS Nano desk-screen will flag this.
- The AI disclosure is in the Acknowledgments instead of the Methods section, against ACS's explicit guidance.
- The disclosure language is generic ("AI tools were used") without specifying tool name, version, and use case; ACS Nano editors return manuscripts with this gap.
- Any figure or schematic representing original research data was generated by AI; ACS Nano prohibits this regardless of disclosure.
Manusights submission-corpus signal for ACS Nano. Of the manuscripts our team screened before submission to ACS Nano and peer venues in 2025, the AI-policy compliance gap most consistent across the cohort is generic disclosure language without tool-version specificity. In our analysis of anonymized ACS Nano-targeted submissions, manuscripts with complete AI disclosure (tool name, version, specific use case, all-author confirmation) clear desk-screen at the same rate as manuscripts without AI use; manuscripts with incomplete or missing disclosure trigger editorial queries that add 1-2 weeks to the timeline. ACS Publications AI Committee reviews disclosures against ICMJE + COPE framework requirements, and ACS Nano applies that framework consistently with ACS's broader policy. Recent retractions in the ACS Nano corpus include 10.1021/acsnano.1c11268, 10.1021/acsnano.0c10395, and 10.1021/acsnano.2c10185. Citing any of these without acknowledging the retraction is an automatic publication-ethics flag, separate from AI-disclosure issues.
What can ACS Nano authors do to stay ahead of AI policy changes?
ACS's AI policy framework continues to evolve as 2026 brings new ICMJE recommendations, COPE guidance refinements, and journal-specific clarifications. ACS Nano authors targeting nanoscale advance submissions should track three signals throughout 2026:
Quarterly policy updates from ACS. ACS Publications AI Committee reviews the AI framework on a rolling basis. ACS Nano authors who pre-register their disclosure language at submission time tend to face fewer revisions during the 2026 transition period than authors who write boilerplate disclosures.
Field-specific clarifications for nanoscale advance. Different research domains see different AI use patterns. ACS Nano's editorial team has been refining what counts as "substantive AI use" versus "ancillary AI assistance" for nanoscale advance work. Authors who err on the side of more disclosure rather than less avoid the publication-ethics gray zone.
Reviewer disclosure norms. As ACS extends AI-disclosure rules to peer reviewers, the response rate from ACS Nano reviewers may shift. Authors should expect that ACS Nano reviewers' use of AI tools is now also disclosed and factored into editorial decisions.
- Manusights internal preview corpus (150+ ACS Nano-targeted manuscripts, 2025 cohort)
Frequently asked questions
Yes, with mandatory disclosure. ACS Nano follows ACS's AI policy under the ICMJE + COPE framework. AI tools can be used for language editing, manuscript preparation, and analysis support, but all use must be disclosed in the Methods section. AI cannot be listed as an author, and human authors bear full responsibility for the content.
In the Methods section. Authors must name the specific AI tool (e.g., ChatGPT-4o, Claude 3.5 Sonnet), its version, and describe how it was used. The disclosure should confirm that all human authors reviewed and take responsibility for the AI-assisted content. ACS Nano's editorial team checks this disclosure during desk-screen.
No. ACS Nano prohibits AI-generated figures, schematics, and images intended to represent original research data. AI tools may assist with figure layout and labeling, but the underlying data and visualizations must come from the actual research. This rule is part of ACS's broader image-integrity policy.
ACS Nano treats undisclosed AI use as a publication-ethics violation following COPE guidelines. Consequences range from required correction to expression of concern or retraction, depending on severity. ACS may notify the authors' institution in serious cases.
The core requirements (disclosure in Methods, no AI authorship, no AI-generated figures) are consistent across ACS-published journals. ACS Nano applies these rules consistently with ACS's broader policy framework. The journal-specific element is enforcement intensity at desk-screen, which at ACS Nano is calibrated by acs nano reviewers expect tem/sem/afm characterization with quantified size distributions.
Sources
- ACS AI policy (accessed 2026-05-08)
- ACS Nano author guidelines (accessed 2026-05-08)
- ICMJE recommendations on AI use (accessed 2026-05-08)
- COPE guidance on AI in research publication (accessed 2026-05-08)
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