Japanese catalyst keeps CO₂-to-methanol byproducts below 11 percent 22%

By Georgina Jedikovska0%

7/7/2026, 3:15:33 PM

BS Summary: This article contains 15 faulty reasoning types, including Unattributed Quote, Overconfidence Bias, and Optimism Bias, with Biased Writer Voice as the most egregious example at 33.2% saturation with 165 hits. Analysis detected 789 faulty-reasoning hits from 497 analyzed words, generating a BS Score of 35.9% and a BS Rank of 22% (16,063 of 20,496 articles). This article is better (less manipulative) than 78.40% of the article peer group.

Scientists in Japan have recently developed a copper nanocluster that can convert carbon dioxide (CO2) into methanol while greatly reducing unwanted byproducts. 
The project was carried out by scientists from Tohoku University in Japan and the Indian Institute of Technology Indore. 
The new approach transforms harmful CO2 into valuable fuels and chemicals under mild conditions. 
The team unveiled that controlling copper atoms changes how CO2 is converted in an electrochemical reaction. 
Instead of producing large amounts of formate, an unwanted byproduct, the newly designed catalyst selectively generates methanol. 
According to the team, the discovery is essential for designing advanced catalysts. 
It shows how atomic-level control can unlock cleaner and more efficient pathways for converting CO2 into valuable fuels. 
Cleaner fuel production 
Copper (Cu) has long attracted attention as an inexpensive and abundant catalyst for converting carbon dioxide. 
It is, in fact, the only monometallic catalyst that can be used in electrochemical CO2 reduction processes for high-value chemicals and fuels. 
However, existing copper catalysts often produce formate alongside the desired products. 
Formate is the salt or ion of formic acid and is widely used in industries such as agriculture and manufacturing. 
Still, during methanol production, it is an unwanted byproduct because it diverts carbon away from methanol formation. 
To tackle the challenge, the team created a sulfide-templated copper nanocluster (S@Cu50S12(StBu)20(CF3COO)12) with a precisely controlled internal structure. 
It featured a unique core-shell architecture composed of an inner S@Cu14S12 core surrounded by an outer Cu36(StBu)20 shell protected by thiolate ligands. 
It enabled the team to tune the ratio of Cu(I) and Cu(II) oxidation states without changing the catalyst’s overall geometry. 
They then compared the catalyst with a previously reported copper nanocluster to determine how the subtle electronic changes affect carbon dioxide conversion. 
Although both catalysts showed similar overall activity, their products were very different. 
A better copper catalyst 
The conventional catalyst produced formate with a Faradaic efficiency of about 38 percent. 
In contrast, the new nanocluster reduced formate formation to below 11 percent and produced methanol with a Faradaic efficiency of about 19 percent at -1.0 volts (V) versus the reversible hydrogen electrode (RHE). 
The older catalyst produced no methanol at all. 
“This work establishes that subtle modulation of the Cu(I)/Cu(II) balance can fundamentally redirect reaction pathways, providing a molecular-level strategy to overcome intrinsic selectivity limitations in Cu NC catalysis,” the team pointed out. 
According to the researchers, adding a sulfide ion at the center of the nanocluster subtly changed its electronic structure. 
It affected how the reaction intermediates interacted with the catalyst surface. 
They believe their findings will provide a new strategy for designing catalysts with atomic-level precision. 
“This study provides the first clear evidence that precise modulation of the copper valence state in Cu nanoclusters can directly influence the selectivity of CO2 reduction pathways,” Yuichi Negishi, PhD, an associate professor at Tohoku University, concluded in a press release. 
The study has been published in the open access journal JACS Au. 
Article reasoning-pattern comparisonThis article: 6.4%Georgina Jedikovska: 1.6%Interesting Engineering: 3.9%Confirmation Bias6.4%This article: 6.6%Georgina Jedikovska: 1.7%Interesting Engineering: 1.2%Anchoring Bias6.6%This article: 3.2%Georgina Jedikovska: 0.8%Interesting Engineering: 2.5%Availability Heuristic3.2%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 1.2%Representativeness Heuristic0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.3%Hindsight Bias0.0%This article: 23.1%Georgina Jedikovska: 15.4%Interesting Engineering: 5.4%Overconfidence Bias23.1%This article: 10.1%Georgina Jedikovska: 5.1%Interesting Engineering: 6.4%Framing Effect10.1%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.2%Loss Aversion0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.6%Status Quo Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.4%Sunk Cost Effect0.0%This article: 11.1%Georgina Jedikovska: 7.9%Interesting Engineering: 16.6%Optimism Bias11.1%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.5%Pessimism Bias0.0%This article: 0.0%Georgina Jedikovska: 0.4%Interesting Engineering: 1.0%Negativity Bias0.0%This article: 3.6%Georgina Jedikovska: 0.9%Interesting Engineering: 4.7%Self-Serving Bias3.6%This article: 3.4%Georgina Jedikovska: 0.9%Interesting Engineering: 0.1%Fundamental Attribution Error3.4%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Actor-Observer Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 1.0%In-Group Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.1%Out-Group Homogeneity Bias0.0%This article: 4.4%Georgina Jedikovska: 1.1%Interesting Engineering: 5.0%Halo Effect4.4%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Horn Effect0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 1.0%Recency Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.2%Primacy Effect0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Blind-Spot Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Ad Hominem0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Straw Man0.0%This article: 10.7%Georgina Jedikovska: 7.4%Interesting Engineering: 9.0%Appeal to Authority10.7%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 1.5%False Dilemma0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.3%Slippery Slope0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.1%Circular Reasoning0.0%This article: 4.4%Georgina Jedikovska: 3.3%Interesting Engineering: 5.1%Hasty Generalization4.4%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.2%Red Herring0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.8%Bandwagon0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 2.4%Appeal to Emotion0.0%This article: 6.4%Georgina Jedikovska: 1.6%Interesting Engineering: 1.3%Begging the Question6.4%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 2.2%Post Hoc (False Cause)0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Tu Quoque0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.6%Burden of Proof0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.2%Appeal to Nature0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.3%Composition/Division0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.7%Anecdotal0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.1%No True Scotsman0.0%This article: 7.8%Georgina Jedikovska: 2.0%Interesting Engineering: 2.7%Ambiguity (Equivocation)7.8%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Middle Ground0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Personal Incredulity0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.2%Special Pleading0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Genetic Fallacy0.0%This article: 24.1%Georgina Jedikovska: 6.0%Interesting Engineering: 1.8%Unattributed Quote24.1%This article: 0.0%Georgina Jedikovska: 1.6%Interesting Engineering: 0.7%Quote-first Misdirection0.0%This article: 33.2%Georgina Jedikovska: 8.3%Interesting Engineering: 3.8%Biased Writer Voice33.2%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.8%Indoctrination0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Georgina Jedikovska: 0.0%Interesting Engineering: 10.6%Attempt to Sell a Product or S…0.0%

497 words analyzed.

Speakers

1speaker8.2%attributed speech456writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 14 words • 100.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 12 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 19 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 15 words • 100.0% coverageYuichi Negishi, PhD • 41 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverage
Selected voice

Yuichi Negishi, PhD

100%flagged-word coverage
41 attributed words100% of attributed speech65% writer coverage
0%50.0%100.0%Unattributed Quote+82.7 ptsWriter: 17.3%Yuichi Negishi, PhD: 100.0%100.0%Biased Writer Voice-36.2 ptsWriter: 36.2%Yuichi Negishi, PhD: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.