A Map Drawn
on Thin Sheets

MXene (pronounced “maxene”) is a very thin sheet. Rows of metal atoms and rows of carbon (or nitrogen) atoms alternate like a sandwich. Many such sheets stack like the pages of a book. The spacing between sheets is measured with X-rays and is typically around 1 nm.

The most studied is Ti₃C₂Tₓ, made of titanium (Ti) and carbon (C). The Tₓ at the end denotes the atoms attached to the sheet surface. How the surface is treated changes the properties considerably. As Chapter 6 will show, even for the same Ti₃C₂, some values spread by more than 3 eV (a very small unit of energy used when dealing with a single electron) depending on the treatment.

Synthesis goes roughly like this. A hard crystal that exists before the sheets form (the precursor) is immersed in a chemical that dissolves out one layer (etching). This exposes the sheets, which are then peeled off one at a time (delamination). There are several etchants: hydrofluoric acid (HF), a mixture of lithium fluoride and hydrochloric acid, molten salts, and so on.

How many kinds are there? Metal–carbon combinations actually synthesized in the lab alone number more than 50. Counting mixed-element ones and ones with different surfaces, the number approaches 100. Candidates predicted only by computer calculation number more than 1,000.¹

“MXenes comprise a very large number of materials. Which composition should be used for which property — can the structures and synthesis methods be organized systematically?”— This map began on 2026-10-06 from this single question.²

MXene first made its name through its ability to store electricity, i.e. its capacitance. Capacitance means how much electricity it can hold, as in a capacitor. The landmark results published in Science in 2013 and Nature in 2014 were both about the capacitance of Ti₃C₂Tₓ. In particular, the key was how much it stores in a given volume (the volumetric value).³

After that, research branched out in every direction: how well it conducts electricity, how well it blocks electromagnetic waves, how hot it gets under light, how well it carries heat. These are called properties. Papers piled up for each property.

The person who asked the question is a researcher who once wrote papers with MXene. In their own words, however, “I didn't study the material itself; I used it to build structures.” So the materials knowledge needed for this map has been built up piece by piece during the work.⁴

The question was simple, but the answer was not. The next chapter shows why.

Numbers are scattered inside the papers. Some sit in the middle of the main text, some in figure captions, and some are bundled with several samples in one sentence via the word “respectively.” To find the value you want, you have to open the papers one at a time.¹

So a few databases that collect only the numbers (data stores that gather values into tables) already exist. The most similar one we found was a public dataset of 573 rows collected from 114 papers. It is a useful resource. But on closer inspection it had four shortcomings.²

• 91% of the values are concentrated in a single MXene, Ti₃C₂.
• The values were extracted by AI, but there is no report of how accurate they are.
• No value records “which sentence of the original it came from.”
• Above all, wrong values were mixed in.

We checked the last point ourselves by opening the originals. We compared the values in that database line by line against the original papers. Values that did not match the original amounted to 17.9% in the first 15 papers and 8.0% in an expanded set of 22 papers. Because the number of papers examined (the sample) is small, neither figure is definitive.³

The wrong values often had the same shape. The value of a composite (two materials combined), made by mixing MXene with another material, was recorded as if it were the value of pure MXene. The number itself is in the original, so at a glance it looks right. But “whose number it is” was wrong.⁴

For a flagship property such as how well electricity flows (electrical conductivity, in S/cm — larger means better conduction), record values like “24,000 S/cm” circulate without their conditions. These are numbers stripped of which sample was made how and measured how. This is exactly where a map with attached evidence is most useful.⁵

This is not a race to collect more values. It is the work of collecting values that can be trusted.— the sentence that defined “our share” of this map

Even obtaining the originals was not easy. From the start, we decided to use only legal routes, because values taken from unauthorized copy sites can neither be published to others nor re-checked by others against the same original.⁶

Yet even for papers that anyone can read openly, publisher websites often blocked programs from accessing and reading them. In the first comparison, of 60 open-access papers the full text could be retrieved by program for only 16. In the end, a paper repository that works like a public library became the legal route a program can read. (Papers that publishers blocked from programs are covered in Chapter 7.)⁷

It is not only competitors. There is also a public dataset of 4,488 MXene structures generated by computer calculation. This is not a competitor but a partner: a “calculated value” column can be placed side by side under the same composition name.⁸

So the task was set. Collect the numbers, but attach to each number the sentence in which it is written. And score separately whether each pair is correct.

A row of this map is not just a number. It records, as one bundle, which MXene (composition), which sample and which unit the number belongs to, and from which sentence of the original it came. The synthesis method and the measurement conditions are recorded too. Like the call number on a library book, anyone can use this row to find the way back to the original location.¹

Look at the sentence in the figure above. It reports surface area (m²/g — how many square metres 1 g of powder would cover if fully spread out). One sentence contains three samples and three numbers. Because the word “respectively” is attached, the pairs must be matched in order. The first sample (few-layer Ti₃C₂Tₓ) is 6.05 m²/g. This kind of pairing confuses people and AI alike.²

Some rows cannot be used even when the number is right. A row extracted from the sentence “extending to 16 hours, the specific surface area became 95.7 m²/g” was one of them. The value 95.7 was exactly as in the original. But the sample-name field was empty. Since there was no way to know which sample the number belongs to, it was not put on the map.³

Even if the number is right, it cannot be used when “whose number it is” is blank.

Eight steps to put one property on the map

Even when the property changes, the order is always the same.⁴

1. Fix the list of papers first. So that favorable papers cannot be cherry-picked in later.
2. Screen. Keep only papers that report the value from “their own measurement.” Papers that only calculate, and papers that copy someone else's value, are excluded.
3. Transcribe. An AI reads each paper and extracts, row by row, the composition, sample name, value, unit and original sentence.
4. Make trap questions. Secretly alter a few of the extracted rows (Chapter 4).
5. Save the scoring criteria first. Before scoring, write down and save what will count as “correct.”
6. Two judges score independently. Each opens the original and picks one of “correct · wrong · can't tell.”
7. Attach a mark. If both say “correct,” ●; if at least one says “wrong,” ✕; otherwise ○. (In Chapter 5 one more mark, ◐, appears.)
8. Decide pass or fail. If ● is 80% or more and ✕ is 10% or less, the round passes.

The fifth step is the backbone of this work. If the criteria are set after seeing the results, the criteria bend toward the results. It becomes not measuring but “fitting.” It is like sealing the answer key in an envelope before the exam. This is called preregistration (registering in advance). If we want to change the criteria, we open a new round and measure again from the beginning.⁵

This creates some inconvenience. A moment comes when, looking at the results, you think “it would look much better if only this field were excluded.” That story is in Chapter 5. Even then, we did not put on the map a number chosen after seeing the results.

Think of grading a school exam. The question paper is the original paper. A student's answer is one row the AI extracted from the original (“this sample's value is this”). There are two teachers grading: Claude and Codex, two AIs built by different companies. Neither sees the other's grading.¹

But how would we know if a teacher grades carelessly? So before trusting the teachers, we test them first; we are “grading the graders.” We secretly mix eighteen trap questions into the pile of answers. Twelve are answers deliberately altered to be wrong, and six are correct answers left untouched. The teachers do not know which ones are traps.

The reason for including the six correct answers is simple. Even a teacher who says “wrong” to everything would catch all twelve altered answers. Only by also checking whether the teacher recognizes intact answers as “correct” can we trust that teacher.

What the traps look like

Some of the traps actually used.²

• Changing the value. In the original, a sample's surface area was 16.7 m²/g. The trap sent it as 61.79. Both teachers caught it, saying “the paper says 16.7.”
• A sample name that doesn't exist. To the value 556.283 m²/g we attached a sample name not in the paper (“composite calcined with graphene, sample B”). Both said “no such sample in the paper.”
• A different MXene. The 34.21% efficiency of converting light into heat (photothermal conversion efficiency) belonged to niobium MXene (Nb₂C) particles. We sent it as Ti₃C₂. Both caught it: “these particles are Nb₂C.”
• Changing the unit. We changed 1.15 nm to 1.15 Å (a unit 10 times smaller). Both said “it is nm, not Å.”
• Blanking a field. We deliberately sent a condition clearly stated in the original as a blank. This tests whether the judge recognizes “omitted” rather than “wrong” (Chapter 5).
• Changing the property name. The number and sample were left as they were, and only “which property the value belongs to” was changed. This is a trap newly added on 2026-10-09 (Chapter 7).

Catching all eighteen traps does not mean “the judge is perfect.” Eighteen is not many. Even if a judge actually errs once or twice in ten (up to about 17%), it could get all eighteen right by chance. So we only go as far as saying “this judge has been tested enough to be trusted.”²

What one misses, the other catches. That is why there are two teachers.

Sometimes the two teachers disagree. For a row extracted from a sentence saying the spacing between layers “increased to about 1.41 nm,” one teacher said “correct” and the other said “wrong — this is the value of the sample before another chemical was added.” The rule is that if even one says “wrong,” it is ✕. That row did not go on the map.³

If the answer key is ambiguous, even the teachers fail

Once, both teachers failed the qualification test. Both caught all twelve altered answers. But of the six “intact correct” answers, one teacher called only four “correct,” and the other only three.⁴

On inspection, the problem lay not with the teachers but with the answer key. The rows we had chosen as “correct” had ambiguous definitions to begin with. Since then, before scoring, we first open the originals of the correct-answer rows. Ambiguous rows, such as a lone number from a table or a value measured in a different liquid, are removed from the correct-answer candidates. In the next round after this fix, both teachers recognized all six correct answers. However, since this is a single comparison with a different property and different papers, we do not claim it was “because of this.”⁵

Conversely, too clean is also suspicious. In one round, both teachers called all 36 rows “correct” and scored perfectly on the traps too. Rather than rejoicing, we first wrote down “were the traps too easy?”⁶

Electrical conductivity, EMI shielding (how well it blocks electromagnetic waves), batteries, interlayer spacing. Even as the property changed, the failure points were similar. The numbers mostly matched the originals. What was wrong were the fields describing the numbers, for example “should the substrate film underneath also count as a mixed-in material?” or “is the sample wet or dry?”¹

Interlayer spacing: failing, then measuring again

Interlayer spacing is the gap between one MXene sheet and the next. In the first scoring, a row counted as “correct” only if it also got “was the sample wet or dry?” right. But papers rarely reported this state. The result was ● 45%, a fail. Most of the ✕ came from exactly this state field. Rows in which the number itself was wrong were only 2.2%.²

Calculating after seeing the results gave “78% if only the state field is excluded.” A tempting number. But it is a yardstick chosen after seeing the results. This number was not used on the map.

Instead, we started over. A new criterion, “exclude the state; check only value, unit, composition and sample,” was saved before scoring, and the two teachers re-scored the same rows. The result was ● 90%. This time it passed. The rows went on the map with a “state not verified” mark.³

Thermal conductivity: failed three times, passed on the fourth

Thermal conductivity is how well heat is carried. It is used to remove heat from devices. This property taught us the most.⁴

First. ● 66.7%, fail. The cause was not the numbers but the instructions we wrote. For the field recording the direction of heat flow, we allowed only two words: “parallel to the sheets” and “across the sheets.” But the papers used other words, such as “horizontal,” “vertical” and “axial.” The transcribing AI left the field blank, and the judges gave ✕, saying “it is stated in the paper but was left blank.” There was also a row in which the unit was forgotten, writing 550 mW as 550. That was our mistake.

Second. Only the instructions were fixed and re-saved, and the same papers were transcribed again. ● 81.2%, above the pass line. But ✕ was 18.8%, above the 10% ceiling, so another fail. This time all sixteen ✕ were blanks. Not a single number was wrong.

Third. We created a new mark on the scoring side, counting “wrong (✕)” and “omitted (◐ — present in the paper, but the field is blank)” separately. Into the traps we also put four “fields deliberately blanked although an answer exists.” Both teachers correctly called all four “omitted” rather than “wrong.” This was the first confirmation that the two could be told apart.

The third result was: ● 62, ◐ 22, ✕ 0. Correct plus omitted is 98.8%. Of 85 rows, not one number was wrong. Yet it is a fail, because omissions were 26%, above the 20% ceiling set before scoring.

The numbers were right in all 85 rows. The only reason for failing: the transcription was not thorough enough.

This ceiling was a safeguard placed on purpose. Counting “omitted” separately can make the scoring lenient: even with many blanks, it is easy to wave them through with “well, it isn't wrong.” So we decided in advance that “if blanks exceed 20%, the transcription is deficient.” And that safeguard actually tripped. We did not lower the ceiling.⁵

What needed fixing was not the scoring but the transcription. We added the instruction: “Before leaving a field blank, search once more in the experimental-methods section and the figure captions.” But we did not rewrite the scores of the finished round. We opened a new round and re-transcribed the same 35 papers from the beginning.

Fourth. ● 70 · ◐ 12 · ○ 2 · ✕ 1, 85 rows in total. Correct plus omitted 96.5%, wrong 1.2%, omitted 14.1%. This time all three conditions were met: pass. Both judges also caught every trap.⁵

However, we do not say “the new instruction reduced omissions.” This time the transcription was redone, and the judges' instructions also changed slightly. We cannot separate what produced the effect. The purpose of this round was to fill the map with more solid values. The thermal-conductivity points on the map were replaced with the 82 points from this passing round.

When both omit the same thing, no one notices

Something similar happened with the battery property. Two transcribing AIs read the same papers independently and extracted values, and there were conditions both had left blank, conditions clearly stated in the experimental-methods section of the original. Two answers being “the same” cannot catch that both “omitted the same thing.” Those blanks surfaced only in the scoring against the original.⁶

And we do not keep rerunning until it passes. We decided not to revise the same question paper more than three times. If you keep revising and rerunning until it works, that becomes not measuring but “fitting.”⁷

The first map started with one property and one MXene, Ti₃C₂: gravimetric capacitance. We collected 351 values from 81 papers. After that we added properties one at a time, and also MXenes other than titanium ones. When work function was added, the map's points (one value = one point) reached 1,952. How it grew further on 2026-10-09 is told in Chapter 7.¹

The cards below explain the ten properties in plain words, with the scoring results attached. A green band means pass, red means fail, and grey means the sample was too small to judge.

How to read the numbers — with some care

A few interesting numbers show up. The median below is the value in the very middle when the values are lined up by size. However, every one of them was computed by mixing values measured under different conditions, so we can only say “this is roughly what is typically reported.” They are not values to compare against each other to say “which is better.”²

• Work function. The median of 40 rows of Ti₃C₂ not mixed with other materials is about 4.53 eV. But the range is 2.85–6.0 eV, spreading by more than 3 eV depending on the surface treatment. This very spread is the raw material for answering “made which way, what is the work function?”
• Surface area. MXene not mixed with anything had a median of about 12.9 m²/g, and composites mixed with other materials about 43.5 m²/g. That does not mean “making a composite gives four times more.” Measurement temperature, gas and synthesis method all differed. The morphology field was also empty in as many as 86 of 152 rows.
• Photothermal conversion efficiency. Median about 39% under 808 nm light, about 42% under 1064 nm light. Concentration, light intensity and the calculation formula differed from paper to paper.
• Gravimetric capacitance. It also contains unusually large values such as 4,000–12,000 F/g. These are values converted from measurements of a very small spot on a single sheet. On the map they are recorded exactly as written in the original.

Do not read the number of points on the map as the amount of research.

The number of points must also be read with care. In the round that collected non-titanium MXenes, 62 rows of Nb₂C and 36 rows of Mo₁.₃₃C came in. But 84 of those rows came from just two papers. Many points do not mean “niobium and molybdenum MXenes have been studied a lot.”³

Still, considering that the first map started with “Ti₃C₂ alone,” the fact that the tiles in the figure above now sit together on one map is itself progress. MXenes mixing several metals, and MXenes using carbon and nitrogen together, also came in with their original sentences attached.⁴

Up to Chapter 6, most of the map's points were titanium (Ti) MXenes: 82.3% of all points. Only one side of the map was dense. So on 2026-10-09 the effort went into filling in MXenes of other metals such as niobium, vanadium and molybdenum.¹

The first batch — failed, but not one number was wrong

First we opened 27 not-yet-used papers among the open-access papers already retrieved by the program. Excluding 12 (papers that only calculate, papers without the desired properties, and so on) left 15. From these, 96 rows were transcribed.

Scoring result: ● 81, ✕ 15. ✕ was 15.6%, above the 10% ceiling, so not passed. But on opening the 15 ✕ rows one by one, the number of rows with a wrong number was 0. All were label problems: “which property is this value of?”

• 6 rows — the length of two sheets' worth (the c lattice parameter) was put in the “spacing between sheets” field. The instructions given to the transcribing AI said “allowed,” but the instructions given to the judges said nothing about it. Our two sets of instructions differed. Both judges said “wrong.”
• 9 rows — supercapacitor (a capacitor that charges very fast) values were put in the “battery capacity” field. We had not created a suitable field for those values. Only one judge said “wrong.”

Still, the verdict was not changed. One can calculate “excluding these two causes, all 81 rows are correct,” but it is a number made after seeing the results, so it is not used as evidence (the envelope rule of Chapter 3). On the map, 80 points went up with the mark “round not passed · both judges say this row is correct.” And one rule was fixed: the term definitions given to the judges are copied verbatim from the sentences given to the transcribing AI.²

The second batch — papers downloaded by hand

There were 137 papers that could not be retrieved because publishers blocked the program. Those that could be opened legitimately were then retrieved by hand, one by one. After removing duplicates, 56 papers.

Papers brought in by a person got no special treatment. They were screened, transcribed, mixed with traps and scored in exactly the same way. This time two new “property-name change” traps were added. Remaining: 29 papers, 253 rows.

Result: ● 253/253. Both ✕ and ◐ were 0. As said in Chapter 4, when it is too clean we suspect first. So we checked the judges again first. Both judges caught all 18 traps, including the newly added “property-name change.” They were not simply calling everything “correct.” However, why this differs so much from the first batch cannot be separated out: the instructions had been fixed, and the batch of papers itself was different.³

“Correct (●)” means “the original was transcribed correctly.” It does not mean “the original is right.”Example: a value whose unit looks odd (62.5 A h g⁻¹) was also “correct,” because it appears exactly so in the original. This value was not in a field that goes on the map, so it was not added.

From this batch, 234 points were added to the map. There are still gaps. 77 paywalled papers have not yet been obtained. Papers that can be obtained by hand are mostly “free to read” ones, so the set may be skewed in that direction.

On the same day, thermal conductivity also passed in its fourth round, as noted at the end of Chapter 5.⁴

17 “candidates for a second look”

We swept the whole map once with an eye to “the usual range.” 17 points came up that are exactly as in the original but far outside the common range: an interlayer spacing of 0.354 nm (usually around 1 nm), a work function of 1.7 eV, an electrical conductivity of 34,520 S/cm, a gravimetric capacitance of 4,000–12,000 F/g, and the like. By category: interlayer spacing 9, two-sheet length (c lattice parameter) 3, capacitance 2, battery 1, electrical conductivity 1, work function 1.

This does not mean they are wrong. They may be values measured under very special conditions. For example, 4,000–12,000 F/g is a value converted from a measurement of a very small spot on a single sheet (Chapter 6). So instead of deleting them, we only attached a “candidate for a second look” mark. And these points were excluded from the test below.⁵

Testing the map — 20 known answers

When you get a new navigation system, you first try a route you already know. If it suggests a wrong route, you cannot trust it for routes you don't know either. The map was tested the same way.

From papers already established in this field, we picked 20 “known facts,” for example “etching with hydrochloric acid and lithium fluoride gives a wider interlayer spacing than etching with hydrofluoric acid.” They were chosen without looking at the map and checked down to the page in the original. The rules for comparing them with the map were also saved before seeing the map's numbers. And the test was run exactly once.

Of the 7 it could answer, 5 agreed with the known facts. The median interlayer spacing on the HCl/LiF-etched side was 13.7 Å, and on the HF-etched side 12.1 Å, exactly as known. The median electrical conductivity of the titanium and vanadium families was 3,612 S/cm, and of the niobium family 31 S/cm, also in the known direction.⁶

1 disagreed. It asked how strongly a “film thin enough to be transparent” resists electricity (sheet resistance), but our map could not separate transparent thin films from thick films, so the two were mixed. It seems the conditions did not match, rather than the map's numbers being wrong. Even so, we did not fix it after seeing the results. 1 was inconclusive, because our map put “polymers added as binders” and “materials mixed in to add function” into the same field.

The remaining 13 could not be answered at all. A comparison needs papers on both sides, but one side had only 0–3 papers. For example, for the capacitance of MXene etched with sodium hydroxide (NaOH) instead of hydrofluoric acid, the map has 0 papers.

The 13 unanswered are not failures. They are the list of what to fill next.

What this test showed is clear. The map's first weakness right now is not “wrong values” but “thin data for comparison.” Even the 5 that agreed go only as far as “no contradiction,” not “proven.” Conditions differ from paper to paper, and 7 is a small number. So showing the map to a leading authority in this field has been postponed. After filling the gaps, it will be tested again with the same 20 questions and the same rules. How far the 13 drops is the first yardstick for “has the map become useful?”

How we chose the papers

Papers anyone can open, first. Every number on the map carries its original sentence. Since other people must be able to check that sentence for themselves, at first we used only full texts that are freely available.¹

Aimed at the places where the map is empty. We chose papers to fill the spots that the usefulness test (Chapter 7) had flagged as “cannot answer — no data here.” For example, a spot such as “the capacitance of MXene etched with sodium hydroxide instead of HF.”

Choosing by title alone misses. At first we chose by the words in titles and abstracts. Then, while looking for “papers that etched with an alkali,” we caught “papers that etched with HF and then washed with an alkali.” Same words, different experiment. So nowadays we also check whether the abstract actually states that condition, and a person reads them once more to filter.²

The test's “answer papers” are kept out. The usefulness test checks whether the map can rediscover 20 “known facts.” If the papers that first stated those facts were put into the map, the map would be getting the answers right by copying the answer key.

Papers brought in by a person are scored the same way. Papers retrieved through a browser include publisher full texts. Still, they are scored by the same method, and the results are reported separately for each “where it came from” stream. We do not throw data away, but we do not blur it by mixing, either.³

Write the rules first, before seeing the results. A rule changed after seeing the results gets the label “exploratory.”

How we changed the rules

① Write it down and save it first. Before running a test, we write “what will be counted · what score passes” in a file and save it (commit it). That way, no one can later change the story to “actually, the criterion was this.”

② If it fails, we do not sit the same test again. On 2026-10-09, while scoring the 45 papers a person had brought in, one judge missed two trap questions: it called rows whose name field had been deliberately left blank “correct.” Re-running the same test sheet would amount to running it until it passes. So that round was left as “undetermined,” and we re-took it with a new test sheet with freshly drawn traps. This time both judges caught all 18 traps, and 222 of 223 rows passed.⁴

③ A rule changed after seeing the results gets a label. In the usefulness test, “a Ti₂C battery has a larger capacity than Ti₃C₂” disagreed. Looking into it, the Ti₃C₂ values in our map included many composites mixed with high-capacity materials such as silicon — whereas the answer paper compared pure MXenes with each other. So we changed the rule to “compare only samples with no mixed-in material.” But because it was changed after seeing the results, we labelled it “exploratory” and do not use it as evidence that the map is useful.⁵

④ Write what improved and what got worse side by side. Re-measuring with the changed rule, the Ti₂C comparison turned into “agrees.” But “Nb₂C is larger than Ti₂C,” which had earlier been “agrees,” became “no answer”: there was only 1 paper with pure Nb₂C data. That “agrees” had been leaning on composite values. Writing only what improved would make the map look better than it is. So we wrote down both.

⑤ The real evidence comes from new data. The changed rule is now frozen, and we will test again with new papers obtained from here on. If it passes then, that will be the evidence that “the map is useful.”

What we still don't know

Mostly only open-access papers were examined. Nearly every round used only full texts that anyone can legally open. The papers retrieved by hand were also mostly free to read (Chapter 7). From the afternoon of 2026-10-09, 30 publisher full texts retrieved through a browser also came in, but most of it is still a story “within open-access papers.” Including paywalled papers could change the picture.²

Boundaries with ambiguous definitions remain. For example: “is a chemical added to separate the sheets an etchant, or a substance that intercalates between the sheets?” One such question was answered by obtaining all 33 foundational papers of this field and consulting their original texts. Boundaries not yet answered carry only a “judgment deferred” mark.³

To be supplemented — what we will fill in when data arrive

“Undetermined” does not mean “wrong.” It means “cannot be said yet with this data.” In such cases we did not lower the criteria to force an answer. Instead, we wrote down what was missing. Below is that list.

• Thickness and electrical conductivity. Some films made by the same method had very different conductivities. We wanted to see whether thickness explained this, but of 300 open-access papers, only 4% reported thickness and conductivity together. The usable papers numbered 12, short of the preset 15. → Measure again when more papers reporting thickness have been gathered.⁴
• The “description fields” of capacitance. This round was “undetermined” not because of the number of papers but for another reason. The “correct rows” of the judge qualification test themselves had ambiguous definitions, so the two judges recognized only 4 and 3 of the 6 correct answers (Chapter 4). Since the judges could not be trusted, the results were not used either. → Re-select the correct rows and measure in a new round.⁵
• The 13 unanswered in the known-facts test (11 when re-measured after filling the empty spots · Chapter 8). The papers on the other side of the comparison numbered only 0–3. To fill: electrical conductivity of HF-made multilayer and powder samples · capacitance of MXene etched with an alkali such as sodium hydroxide · capacitance of composites mixed with a polymer (e.g. polypyrrole) · battery capacity of Ti₂C and Nb₂C · light-to-heat conversion of Ta₄C₃ · interlayer spacing of MXene made with another fluoride chemical (NH₄HF₂). → Find open-access papers, put them through the same scoring, then test again with the same 20 questions (Chapter 7).⁶
• Splitting two fields of the map. Split the “mixed-in material” field into “polymer added as a binder” and “material mixed in to add function.” Add a “transparent thin film” mark to the morphology field. These caused the two questions in the known-facts test that were inconclusive or disagreed.⁶
• Routes of synthesis (13 October). When we first built the map, only the hydrofluoric-acid (HF) family had its own columns; everything else went into “other”. A researcher in the field then pointed out that MXene is no longer made with HF alone — molten salts, milder etching with ammonium chloride, organic and inorganic etchants are all in use. So we split the 105 “other” points again: alkali (e.g. sodium hydroxide) 38 · fluoride-free etching such as iodine or iron chloride 14 · electrochemical etching 4. Growing MXene directly from gases (vapor-phase synthesis) had no values at first; that evening, 45 open-access papers on newer routes went through the same checks and the first 14 vapor-phase values (Ti₂CCl₂, Ti₂NCl₂) came in. Most of the 27 papers received that day also used the HF family. → We will collect more papers that measure MXenes made by the newer routes.
• Volumetric capacitance. The very value that made MXene famous, yet only 15 values in 7 open-access papers were directly measured. Even with a perfect score, “insufficient evidence.” → More papers with direct measurements are needed (Chapter 6).
• 77 papers not yet obtained. Papers blocked by publishers. → Find a way to obtain them by hand. Asking authors directly is an action that goes outside, so the person who commissioned this work decides.⁷

The map is not a discovery. Discovery happens when a lab actually makes what a gap on the map points to.

Gaps are starting to show

Building the map revealed a “gap” for the first time. For the capacitance of MXene made by exposing the sheets with molten salt (molten-salt etching), there were 0 papers within the open-access papers we examined. Either no one has measured it, or if someone has, it is not in open-access papers. Finding and showing such gaps is what this map ultimately aims to do: to be “a place that helps discovery.”⁸

Human checks — colleagues first, the leading authority last

If the two AI judges make the same mistake together, no one notices. So the final check is done by people. Rules to follow have been set as well.⁹

• No one is asked about their own papers. Values from papers the professor co-authored will not be sent to the professor for checking.
• The AI's answers are not shown. So that the checker is not pulled along by the AI scoring.
• Keep the burden small. 30–60 rows per property, three buttons: “correct · wrong · ambiguous.” If 60 rows are checked and none is wrong, we can say the actual error rate does not exceed about 5%.

The check screen has already been built and stored. But as the test in Chapter 7 showed, now is the time to fill in comparison data first. Asking the leading authority is postponed until after that.

Next steps

1. Fill the gaps. Find the data in the “to be supplemented” list above in open-access papers and add them through the same scoring.
2. Split two fields. Split “mixed-in material” in two, and add “transparent thin film” to morphology.
3. Repeat the same test. Ask the 20 known facts again with the same rules, and see how far “no answer 13” drops.
4. Pair with calculated values. Place the 4,488 computer-calculated structures side by side under the same composition names.
5. A person decides on release. Whether to release the map to the outside is decided by the person who commissioned this work. Originals are included only as short quotations and paper identifiers.

We decided not to inflate the goal. The realistic goals are two: to become a widely used tool, and to become a door to collaborative research.¹⁰

There was already one small signal. That reply was a motivation for this work, not its purpose. The purpose is the same as at the start.¹¹

A map that answers “if I need this property, which MXene should I make, and how?”, with every single value confirmed by its original sentence.

Where it came from — four research notes I wrote earlier

All four are preprints on arXiv. They share one theme: how to check the work an AI has done so that it can be trusted.¹

CCR — When an AI reviews text it produced within the same conversation, it is poor at catching its own mistakes. So I had it review in a new conversation, and it caught more. → This is where the map's design came from: separating the AI that extracts values from the AI that checks them, and not letting the checker see the extraction record.

D-CCR — Then does exchanging reviews over many rounds make things better? Instead, false alarms increased (the title is “More Rounds, More Noise”). → This is one of the reasons we promised “we do not sit the same test again until it passes” (Chapter 8).

CCV (HCCA) — I used the method of checking in a separate, isolated conversation to tell whether an AI gets test questions right by memorizing the answers. → It is the same idea as keeping the “answer papers” out of the map in the usefulness test.

XMR — When does it help to have an AI from a different company do the second check? → This is why the map has two judges, Claude and Codex.

It was not carried over as is. The four papers dealt with errors in text and code; this map deals with scientific values. I only borrowed a few ideas. Conversely, what was newly learned from this map — testing the judges first with trap questions, and writing down the rules before seeing the results — can be given back to the next verification study. The two lines of work correct each other.

Call a lake a sea, and you will find the sea in the lake; call a rock a jewel, and you will carve the jewel out of the rock.

Where it is going — a dream

For now it is MXene alone. But the same method can be used for other thin-sheet materials (2D materials). Further out, could even far more complex subjects, such as the brain, be drawn as 3D maps that AI gathers and people check? For now it is a dream — a dream to be pursued, as this book has shown, by honestly separating “verified” from “not yet” at every step.

Why do it

The words above are my own. They are the spirit of believing something is there, even though it may not be, and carving it out to the end. And one more thing — a stuck problem gets solved in an unexpected place. The numbers of a wholesale market, the numbers of a material, someday even a map of the brain: things that seem unrelated are connected like a spider's web. Finding those connections is science, and it is the reason to keep trying. That is also why I made “Songjarvis,” the AI colleague that built this map with me — not as a tool that only does what it is told, but as a colleague that looks widely and comes to realize things on its own.

My heart in doing all of this is simple: to leave traces that are useful to many people, and to be someone the next generation can look at without shame.