# SEM/TEM Particle Size & Nanomaterial Screening (`jovianlabs/sem-tem-particle-size`) Actor

Particle size distribution from SEM/TEM images (D10/D50/D90 with 95 % CIs, Feret, quality checks, PDF report) plus an EU nanomaterial definition screening: share of particles ≤ 100 nm, with uncertainty. Free demo: run with no image URLs.

- **URL**: https://apify.com/jovianlabs/sem-tem-particle-size.md
- **Developed by:** [Jovian Labs](https://apify.com/jovianlabs) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1,000.00 / 1,000 images

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## SEM/TEM Particle Size & Nanomaterial Screening

Turn scanning or transmission electron microscope images into a particle size distribution report in about ten seconds per image.

**Try it in one click:** leave the image list empty and press Start. The Actor generates three synthetic SEM images with known particle sizes, analyses them and shows how close the measurement is to the truth. Demo runs are free.

Give it the images of one sample. Original TIFF files from Zeiss or Thermo Fisher (FEI) microscopes are calibrated automatically from their metadata; for other images, give the length of the scale bar or the pixel size. It returns D10, D50 and D90 with 95 % confidence intervals, Feret diameters, shape factors, a quality check, a screening against the EU definition of nanomaterial and a PDF report you can file or send.

### EU nanomaterial screening

Is your powder a nanomaterial under the EU definition (Commission Recommendation 2022/C 229/01)? Criterion (a) is met when 50 % or more of the particles, counted by number, have at least one external dimension between 1 and 100 nm, and electron microscopy is the reference way to count them. Every run now answers with numbers, not just a yes or no:

- the share of particles whose smallest dimension (minimum Feret diameter) is 100 nm or less, with its 95 % confidence interval;
- an interval that also allows for calibration error and edge blur;
- the median minimum Feret diameter with its confidence interval;
- a verdict: **meets**, **not met**, or **borderline** when the interval includes 50 %, so you know when more images or a complementary method are needed;
- warnings when fewer than 500 particles were measured, when the magnification is too low near 100 nm, when many agglomerates were flagged or when the smallest particles may be missed.

This is a screening for R\&D, product stewardship and pre-registration checks. It does not assess the volume specific surface area (materials below 6 m²/cm³ are not nanomaterials), dimensions below 1 nm, or the thickness of platelets lying flat, and it does not replace a regulatory determination.

### Who it is for

- R\&D teams working on powders, pigments, catalysts, nanoparticles, ceramics or battery materials
- Labs that already have SEM/TEM images and spend hours measuring particles by hand in ImageJ
- AI agents that need particle statistics from microscope images (callable through the Apify MCP server)

### What you get

| Output | Content |
|---|---|
| Dataset item | particles counted, D10/D50/D90, mean and standard deviation (nm), 95 % confidence interval on D50, precision reached, particles needed for ±2 %, quality status and checks, EU nanomaterial verdict, share of particles ≤ 100 nm and its interval, median minimum Feret |
| `REPORT.pdf` | results, confidence intervals, nanomaterial screening, distribution and cumulative curve, per-image results, quality checks, method, traceability |
| `particles.csv` | one row per detected object: status, equivalent diameter, min/max Feret, aspect ratio, circularity, solidity, position |
| `control-sheet.jpg` | every image with the detected outlines, to check the segmentation at a glance |
| `summary.json` | all statistics in machine-readable form |

### How it works

1. The pixel size is read from the microscope metadata (Zeiss SmartSEM, Thermo Fisher/FEI TIFF), or measured on the scale bar of the image data band, or given by you. When both metadata and scale bar are available they are cross-checked.
2. Particles are segmented (Gaussian smoothing, Otsu threshold, morphological cleaning) and touching particles are split with a distance-transform watershed.
3. Objects cut by the image border are excluded. Likely agglomerates (low solidity or aspect ratio above 2) are flagged and excluded from the statistics.
4. Number-based statistics are computed. Confidence intervals come from 10,000 bootstrap resamples with a fixed seed, so the same images always give the same numbers.
5. Nine automatic checks rate the run green, amber or red: scale consistency (between images, metadata and scale bar), particle count, statistical precision, resolution, agglomerates, border losses, image-to-image consistency, contrast and saturation.
6. The nanomaterial screening counts the kept particles whose minimum Feret diameter is 100 nm or less. A Wilson interval covers sampling; a systematic band covers calibration (at least ± 2 %) and edge blur (± 2 pixels). The verdict is "meets" or "not met" only when the whole interval is on one side of 50 %, and it is set to "borderline" when counting the flagged objects would change the conclusion.

### Accuracy

- **Synthetic SEM images with known particle sizes** (the free demo): 580 particles measured for 578 present; D50 within +3.7 % of the truth.
- **Nanomaterial screening on synthetic samples** with a known share of particles at or below 100 nm: measured within 1 to 6 points of the truth (true 96 %, 70 %, 62 % and 10 %), always inside the interval with uncertainties; the 62 % sample, close to the threshold, is correctly reported as borderline.
- **Limit: strongly agglomerated powders.** When touching particles cannot be told apart, some are measured as one, and D50 reads 10 to 18 % high on such samples. The agglomerate check then turns amber or red: disperse the sample better, or ask for the verified service.

### Input example

```json
{
  "imageUrls": [
    "https://example.com/sample-A-field1.tif",
    "https://example.com/sample-A-field2.tif",
    "https://example.com/sample-A-field3.tif"
  ],
  "scaleBarNm": 200,
  "sampleName": "SAMPLE-A"
}
```

Set `"reportLanguage": "fr"` for a report in French.

Use 3 to 10 images of the same sample, taken at the same magnification, so that 300 or more particles are measured (500 or more for the nanomaterial screening). Images must be direct download links (TIFF, PNG or JPG, up to 60 MB each). Send the original TIFF files when you can: they carry the calibration. `scaleBarNm` applies to all images of the run, so do not mix images whose scale bars show different values unless they are original TIFF files.

### Pricing

You pay per image analysed. There is no charge for images that cannot be downloaded, and demo runs (no image URLs) are free.

### Good to know

- Works best on well-separated, compact particles on a contrasting background. Elongated fibres, porous aggregates and overlapping layers are flagged rather than measured.
- Measurements are 2D projections. Thickness is not measured.
- One sample per run, with a single particle population. Mixed populations at very different scales need separate runs at suitable magnifications.
- This is automated image analysis, not an accredited (ISO/IEC 17025) laboratory measurement.
- Need a report reviewed and signed by a materials imaging scientist? A verified analysis service is available on request (contact through the Actor's issues tab).

# Actor input Schema

## `imageUrls` (type: `array`):

Direct links to the images of ONE sample (TIFF, PNG or JPG), all taken at the same magnification. 3 to 10 fields of view usually give 300+ particles. Leave empty to try the Actor on built-in synthetic demo images (free).

## `scaleBarNm` (type: `integer`):

Length printed on the scale bar of the image data band, in nanometres (e.g. 200). The bar is detected automatically. Optional for original TIFF files from Zeiss or Thermo Fisher (FEI) microscopes: the pixel size is then read from the file metadata and cross-checked with the scale bar.

## `pixelSizeNm` (type: `string`):

Use when the images have neither a data band nor microscope metadata, e.g. 1.6. Overrides everything else.

## `sampleName` (type: `string`):

Shown on the report.

## `reportLanguage` (type: `string`):

Language of the PDF report and CSV headers.

## `maxImages` (type: `integer`):

Each analysed image is billed once.

## Actor input object example

```json
{
  "sampleName": "SAMPLE-01",
  "reportLanguage": "en",
  "maxImages": 20
}
```

# Actor output Schema

## `results` (type: `string`):

Summary statistics of the run: particles counted, D10/D50/D90 in nm, 95 % confidence interval on D50, precision and quality status.

## `report` (type: `string`):

Full report: results, confidence intervals, distribution, per-image results, quality checks, method and traceability.

## `controlSheet` (type: `string`):

Every image with the detected particle outlines, to check the segmentation at a glance.

## `particles` (type: `string`):

One row per detected object: status, equivalent diameter, min/max Feret, aspect ratio, circularity, solidity, position.

## `summary` (type: `string`):

All statistics in machine-readable form.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("jovianlabs/sem-tem-particle-size").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("jovianlabs/sem-tem-particle-size").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call jovianlabs/sem-tem-particle-size --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,jovianlabs/sem-tem-particle-size"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/CFa1Mkfj0V0Qkeg4Y/builds/ab1ccnNXzSzIl1Tn1/openapi.json