跳到正文
cccskills
免费在 GitHub 公开

advmat-methods

Use when deciding what characterization and experimental detail an Advanced Materials manuscript must show to prove its structure–property claim, and what belongs in the Experimental Section and Supporting Information. Partitions and stress-tests methods; does not design figures or run analysis.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(1)

  • SKILL.md5.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Advanced Materials Methods & Characterization (advmat-methods)

When to trigger

  • Your structure or phase assignment rests on a single technique
  • A reviewer of a prior draft asked "how do you know it is this phase / this mechanism?"
  • The device metric has no benchmarking against comparable state-of-the-art systems
  • The Experimental Section sprawls, or you cannot tell what belongs in the body vs. the SI
  • Reproducibility is thin: batch-to-batch spread, synthesis provenance, or statistics are missing

The multi-technique principle

An Adv. Mater. claim about a material must be triangulated by complementary techniques, not asserted from one. A single XRD pattern or one TEM image is rarely enough; referees expect the structure–composition–property picture to close from several independent angles.

A practical characterization matrix (adapt to your material class):

Claim you are makingDo not rely on one technique — triangulate with
Crystal structure / phaseXRD (+ Rietveld) and electron diffraction (SAED) / HRTEM lattice
Morphology / size / architectureSEM and TEM (+ statistics over many particles, not one hero image)
Composition / oxidation stateXPS and EDS/EELS mapping and ICP-MS/AES for bulk stoichiometry
Local bonding / defectsRaman / FTIR / XAS (XANES/EXAFS), NMR where applicable
Optoelectronic propertyUV-vis / PL / TAS + the transport or device measurement it predicts
Electrochemical performanceCV + galvanostatic cycling + rate/stability data + post-mortem analysis

State uncertainties and sample counts. "n = 3 devices, mean ± s.d." beats a single best cell.

Benchmarking is not optional

Adv. Mater. device claims live or die on fair comparison to the state of the art. Put the comparison in the paper, on equal footing:

  • Compare against the best reported systems for the same function, under comparable conditions — cite them.
  • Report the metric with its measurement protocol (scan rate, illumination, active area, humidity) so it is comparable.
  • If you claim a record, show a benchmark table or plot (e.g. an efficiency-vs-stability or Ragone-style comparison) rather than a bare number.

Body vs. Experimental Section vs. Supporting Information

Detail typeMain text / Experimental SectionSupporting Information
Core synthesis route + key parametersExperimental Section (reproducible)full optimization sweeps
The decisive characterization proving the claimmain-text figureextended datasets, more samples
Device fabrication + measurement protocolExperimental Sectionfull protocols, control devices
Benchmarking vs. state of the artmain textfull comparison table + refs
Statistics on the headline metricmain text (n, mean ± s.d.)per-sample data, histograms
Instrument models / settingsExperimental Section (concise)exhaustive parameters

The Experimental Section must let a competent group reproduce the material. Detail that is essential-but-bulky (full recipes, calibration, secondary controls) goes to the SI and is cited inline.

Rigor without bulk

  • Pre-empt the leading alternative explanation for your structure/property claim in the body, briefly, and point to the SI for the exhaustive checks.
  • Report purity/phase fraction, not just "phase-pure" as an adjective.
  • Give data-availability and, where relevant, computational details (see resources/external_tools.md for the DFT/simulation stack).

Checklist

  • Every structural/compositional claim is supported by ≥2 complementary techniques
  • Morphology/size claims carry statistics, not a single hero image
  • The headline metric is benchmarked against cited state-of-the-art systems, fairly
  • Device metrics report the full measurement protocol (area, conditions, scan rate)
  • The headline number carries a sample count and spread (n, mean ± s.d.)
  • The Experimental Section is reproducible; bulky detail is in the SI and cited
  • The leading alternative explanation is named and addressed

Anti-patterns

  • A single-technique structure claim ("XRD confirms the phase") with no corroboration
  • One flattering TEM/SEM image standing in for a distribution
  • A record metric with no benchmarking and no measurement conditions
  • Reporting one best device and hiding batch-to-batch variability
  • An Experimental Section too vague to reproduce, or so long it belongs in the SI
  • "Data available on request" instead of a concrete availability statement

Output format

【Central material claim】...
【Triangulation】technique 1 / technique 2 / technique 3 — sufficient?  yes / add: ...
【Morphology statistics】present?  yes / fix
【Benchmarking vs. SOTA】in main text, fair, cited?  yes / fix
【Metric statistics】n + spread stated?  yes / fix
【Reproducibility】Experimental Section reproducible?  yes / fix
【Next】advmat-figures (characterization figure) or advmat-supplementary (SI partition)

Data-availability, reproducibility, and characterization-reporting expectations evolve — verify current Wiley Advanced Materials policy on the official author page.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12952026年9月27日 更新

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12952026年9月27日 更新

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12952026年9月27日 更新

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12952026年9月27日 更新

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12952026年9月27日 更新

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

中文简介正在准备中。当前显示原文说明。

brycewang-stanford/Awesome-Journal-Skills12952026年9月27日 更新

查看 brycewang-stanford 的全部技能

报告此技能的问题