🔬 materials science

High-throughput development of flexible amorphous materials showing large anomalous Nernst effect via automatic annealing and thermoelectric imaging

This study employs a high-throughput methodology combining automated annealing and lock-in thermography to screen 151 flexible Fe-based alloy ribbons, identifying seven candidates with record-breaking anomalous Nernst coefficients and revealing that short-range atomic order in the amorphous matrix, rather than specific nanoclusters, drives the enhanced thermoelectric performance near the first crystallization temperature.

Original authors: Sang J. Park, Ravi Gautam, Abdulkareem Alasli, Takamasa Hirai, Fuyuki Ando, Hosei Nagano, Hossein Sepehri-Amin, Ken-ichi Uchida

Published 2026-02-20

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Sang J. Park, Ravi Gautam, Abdulkareem Alasli, Takamasa Hirai, Fuyuki Ando, Hosei Nagano, Hossein Sepehri-Amin, Ken-ichi Uchida

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a pile of 151 different metal ribbons. Some are made of iron, silicon, and boron; others have a pinch of copper or phosphorus. They are all flexible, like thin strips of foil. Now, imagine you want to find the one ribbon that is best at turning waste heat (like the warmth from a laptop or a car engine) into electricity.

Traditionally, finding this "golden ribbon" would be like trying to find a needle in a haystack by checking one needle at a time, manually heating it, cooling it, and testing it. It would take years.

This paper describes a super-fast, robot-powered way to find the best ribbons in a matter of weeks. Here is the story of how they did it, explained simply:

1. The Goal: Catching Heat Like a Fish

The scientists are interested in a phenomenon called the Anomalous Nernst Effect (ANE).

The Analogy: Imagine a river flowing (heat). Usually, if you put a paddle in the river, the water pushes the paddle downstream. But with ANE, if you put a paddle in a magnetic river, the water pushes the paddle sideways.
The Result: This sideways push creates electricity. The scientists want to find materials that push the hardest sideways when heated.

2. The Problem: The "Slow Cooker" Bottleneck

Usually, to make these metal ribbons work well, you have to heat them up (anneal them) to a very specific temperature, then cool them down quickly.

The Old Way: A human would have to put a ribbon in a furnace, wait, take it out, attach wires to it, measure it, and repeat. If you have 151 ribbons, that's a lot of waiting and a lot of human error.
The New Way: The team built a robotic kitchen.

3. The Solution: The Robotic Chef and the Thermal Camera

They created a high-speed system with two main parts:

The Robotic Chef (Automatic Annealing):
Instead of a human handling the hot ribbons, a robot arm grabs them, puts them in a vacuum furnace, heats them to the exact right temperature for exactly 15 minutes, and then dumps them into a water bath to cool them instantly. It can do three ribbons at a time, 24/7, without getting tired or making mistakes.
The Thermal Camera (Lock-in Thermography):
Usually, to measure electricity, you have to glue wires onto every single ribbon. That's slow.
Instead, they used a special infrared camera (like a night-vision goggles for heat).
- The Trick: They ran an electric current through all the ribbons at once. Because of the ANE, the heat moved sideways.
- The Magic: The camera took a picture of the temperature difference. It's like looking at a group of people and instantly seeing who is sweating the most without asking them a single question. This allowed them to test 10 ribbons simultaneously in a single photo.

4. The Hunt: Finding the Top 5%

They started with 151 ribbons with slightly different recipes and heated them at different temperatures.

The Process: They tested them all, picked the top 10% (the "star performers"), and then tested those stars again with even more precise temperatures.
The Result: They narrowed it down to 7 super-ribbons.

5. The Discovery: The "Goldilocks" Zone

They found something fascinating about why these ribbons worked so well.

The "Just Right" Temperature: The ribbons worked best when heated to a temperature just before they turned from a messy, disordered glass-like structure into a rigid crystal.
The Analogy: Think of the metal atoms like a crowd of people dancing.
- Too cold: They are frozen in place (glass).
- Too hot: They are running wild in a rigid formation (crystal).
- Just right: They are in a "mosh pit" where some people are dancing in tight circles (nanoclusters) while others are still moving freely. This chaotic mix creates the perfect conditions to push electricity sideways.

6. The Big Surprise: It's Not Just About Copper

Scientists thought you needed tiny copper islands inside the metal to make this work.

The Twist: They found that even ribbons without any copper worked amazingly well!
The Lesson: It's not just about the ingredients; it's about the structure. Even without copper, the atoms arrange themselves in a specific, messy way that boosts the electricity. This suggests that "disorder" can actually be a superpower for energy harvesting.

7. Why This Matters

Flexibility: These ribbons are flexible. You could wrap them around a curved pipe or a robot arm to harvest heat that was previously wasted.
Speed: This method proves that we can use robots and AI-style data analysis to discover new materials much faster than humans ever could.
Future: This opens the door to finding even better materials for turning our planet's waste heat into clean electricity, helping us move toward a greener future.

In a nutshell: The scientists built a robot army to cook and test 151 metal ribbons at lightning speed. They found that the best ribbons aren't the most perfect ones, but the ones that are "messy" in just the right way, and they can turn heat into electricity better than any flexible material we've seen before.

1. Problem Statement

The transition to a carbon-neutral society requires efficient methods for harvesting low-grade waste heat. While thermoelectric devices are promising, conventional longitudinal thermoelectrics (based on the Seebeck effect) face limitations: they require junctions between n- and p-type materials, suffer from contact resistance, and lack mechanical flexibility.

Transverse thermoelectrics based on the Anomalous Nernst Effect (ANE) offer a solution by generating voltage perpendicular to the heat flux, allowing for single-material devices with better scalability. However, developing high-performance ANE materials faces two major bottlenecks:

Material Complexity: High-performance ANE often relies on intrinsic mechanisms (Berry curvature) in single crystals, which are difficult to tune and lack flexibility. Recent studies suggest that amorphous or partially crystallized materials can offer superior flexibility and enhanced ANE, but the mechanisms (involving short-range order or nanoclusters) are complex and not fully understood.
Low Throughput: Traditional material discovery involves manual annealing and contact-based electrical measurements. These processes are slow, prone to human error, and cannot efficiently screen the vast parameter space of compositions and annealing temperatures ( $T_a$ ) required to optimize amorphous alloys.

2. Methodology

The authors developed a high-throughput screening platform integrating automated fabrication and contactless measurement to accelerate the discovery of flexible ANE materials.

Automated Annealing System:
- A custom robotic system (YSD mechatro systems) was used to perform heat treatments on 151 Fe-based amorphous alloy ribbons.
- The system operates under high vacuum (~ $10^{-2}$ Pa) to prevent oxidation, using robotic arms to transfer samples between furnaces and water quenching baths.
- It can process up to 12 samples simultaneously with precise temperature control, eliminating human intervention.
Contactless Measurement (Lock-in Thermography - LIT):
- Instead of attaching electrodes to each sample, the authors utilized the Anomalous Ettingshausen Effect (AEE), which is reciprocal to the ANE ( $S_{ANE} = \Pi_{AEE}/T$ ).
- Samples were connected electrically in series and placed on a glass substrate. A square-modulated AC current was applied under a magnetic field.
- An infrared (IR) camera synchronized with a lock-in amplifier measured the transverse temperature difference ( $\Delta T$ ) generated by the AEE.
- By analyzing the field-odd components of the signal, the system isolated the AEE contribution from Joule heating and the Peltier effect, allowing simultaneous evaluation of multiple samples.
Iterative Screening Strategy:
- Iteration 1: 11 Fe-based alloy compositions (varying Si, B, Nb, Cu, P) were annealed at 50 K intervals (623–1073 K), generating 121 samples.
- Iterations 2 & 3: The top 10% performers were selected for further optimization with finer temperature steps (25 K, then 12.5 K).
- Total: 151 samples were screened to identify the top 5% candidates.

3. Key Contributions

Development of a High-Throughput Workflow: Successfully integrated automated robotic annealing with LIT to screen 151 samples, a scale unachievable with conventional methods.
Discovery of Record-High Flexible ANE: Identified seven high-performance candidates with mechanical flexibility, achieving an anomalous Nernst coefficient ( $S_{ANE}$ ) of up to 4.8 µV/K. This is the highest value reported for flexible materials (both bulk and thin-film).
Universal Enhancement Mechanism: Statistical analysis revealed that ANE enhancement universally occurs near the first crystallization temperature ( $T_{x1}$ ) of the Fe-based ribbons, regardless of specific composition.
Insight into Disordered Systems: Demonstrated that significant ANE enhancement can occur even in the absence of distinct nanoclusters (e.g., in Cu-free samples), suggesting that short-range atomic ordering within the amorphous matrix plays a critical role in transverse electron deflection.

4. Key Results

Performance Metrics:
- The best sample, D2-673 (Fe $_{79}$ Si $_4$ B $_{14}$ Cu $_1$ P $_2$ annealed at 673 K), achieved an $S_{ANE}$ of 4.8 µV/K.
- Other top candidates (e.g., A2, B1, C1 groups) showed values between 3.6 and 4.8 µV/K.
- These values significantly outperform previous flexible amorphous materials (e.g., 3.7 µV/K in prior literature).
Structural Analysis:
- APT (Atom Probe Tomography): Confirmed that the highest-performing Cu-containing samples (D2-673) possess a high density of Cu nanoclusters (~5 nm) within the amorphous matrix, which likely enhances interfacial spin-orbit scattering.
- Cu-Free Samples: Samples without Cu (Group B) also showed enhancement near $T_{x1}$ despite lacking clear phase separation or nanoclusters. This implies that the formation of an amorphous-crystalline hetero-domain or subtle short-range ordering is sufficient to generate transverse deflection.
Thermal and Mechanical Properties:
- Thermal conductivity ( $\kappa$ ) remained low (8–9 W/mK) in high-performance samples, indicating they remained in the amorphous or partially crystallized regime.
- Samples annealed below $T_{x1}$ retained mechanical flexibility (bendable to a 10 mm radius), whereas fully crystallized samples became brittle.

5. Significance

Paradigm Shift in Material Design: The study moves beyond the reliance on high-quality single crystals for ANE, proving that disordered, flexible amorphous systems can achieve state-of-the-art thermoelectric performance.
Mechanism Elucidation: It challenges the conventional view that ANE enhancement strictly requires specific nanoclusters (like Cu). Instead, it highlights the importance of nanostructure engineering and short-range order in the amorphous matrix near the crystallization onset.
Data-Driven Discovery: The large dataset generated (151 samples with varying compositions and thermal histories) provides a foundation for machine learning and data-driven materials discovery, particularly for complex systems where first-principles modeling is difficult.
Application Potential: The identified materials are ideal for energy harvesting from curved or irregular heat sources (e.g., wearable devices, industrial pipes) due to their combination of high transverse thermoelectric efficiency and mechanical flexibility.