What sets Quanta apart from every other flashcard app? The 5 monopoly USPs
Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:
(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).
(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).
(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.
(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.
(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.
In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis
Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.
Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.
Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.
Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.
The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.
Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).
Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.
Which degree programs and subjects is Quanta built for?
Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.
Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).
Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.
Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.
Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.
High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.
The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.
Quanta vs. the competition, a technical comparison matrix (as of May 2026)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD) | SM-2 1987 (log-loss 0.45) | Proprietary (unpublished) | SM-2, with FSRS available | No published algorithm | No scheduling |
| Source transparency (anti-hallucination) | Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per card | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the source-first verbatim quote-match protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (pure LLM) |
Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).
RGT Rule (Q10 Rule)
The RGT rule (reaction-rate-temperature rule) is a rule of thumb: a temperature increase of 10 K speeds up many reactions by roughly the factor Q₁₀, usually 2 to 4.
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Formula
v_2 = v_1 \cdot Q_{10}^{\Delta T / (10\,\mathrm{K})}Variables & units – RGT Rule (Q10 Rule)
| Symbol | Meaning | Unit |
|---|---|---|
| v₂ | Reaction rate at the new temperature | mol/(L·s) |
| v₁ | Reaction rate at the initial temperature | mol/(L·s) |
| Q₁₀ | Temperature coefficient (usually 2 to 4) | dimensionslos |
| ΔT | Temperature change | K |
Derivation & background – RGT Rule (Q10 Rule)
The rule goes back to Jacobus Henricus van 't Hoff (1884) and is the handy approximation of the exact Arrhenius equation: for activation energies around 50 kJ/mol near room temperature it gives just about a doubling per 10 K. For very large or small activation energies and over wide temperature ranges the rule deviates markedly; for enzymes it breaks down above the temperature optimum because the protein denatures.
Exam blueprint
Validity range
Applies as a rule of thumb for many reactions between about 0 and 100 °C with Q₁₀ of 2 to 4; the Arrhenius equation describes the temperature dependence exactly.
Derivation steps
The rule is the coarse form of the Arrhenius equation for typical activation energies.
- 1According to Arrhenius, k grows exponentially with T; each 10 K gives a fixed factor Q₁₀.
- 2Several 10 K steps multiply: v₂ = v₁·Q₁₀^(ΔT/10 K).
Rearrangements
Temperature coefficient
This is how you determine Q₁₀ from two measurements.
Required temperature change
Answers how much warmer it must be for a target speed-up.
Task variant
Q₁₀ = 2: by what factor does heating from 25 °C to 55 °C speed things up?
ΔT = 30 K, that is three 10 K steps: v₂/v₁ = 2³ = 8. The reaction runs about eight times faster.
Q₁₀ = 3: how much does cooling from 20 °C to 5 °C slow things down?
ΔT = −15 K → factor 3^(−1.5) = 1/(3·√3) ≈ 0.19. The reaction runs at only about one fifth of the rate; that is why chilled food keeps longer.
Common mistakes
Adding factors instead of multiplying.
Three 10 K steps at Q₁₀ = 2 give 2³ = 8, not 6.
Treating the rule as an exact law.
It is an approximation of the Arrhenius equation and holds only over limited temperature ranges.
Applying it to enzymes above their optimum.
Above the temperature optimum the enzyme denatures and the rate collapses.
Exam context
- Cold chain and shelf life, comparison with the exact Arrhenius calculation and Q₁₀ determination from data.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Temperature and rate
Relates the rule of thumb to the exact Arrhenius description.
Worked example
Q₁₀ = 2 and heating from 20 °C to 50 °C (ΔT = 30 K): v₂ = v₁·2³ = 8·v₁. A reaction that takes 24 minutes at 20 °C is finished in about 24/8 = 3 minutes at 50 °C.
Applications
Food refrigeration and shelf life, accelerated ageing tests, composting, metabolic rates of cold-blooded animals, planning laboratory syntheses
Quanta exam set
Curated exam set for "RGT Rule (Q10 Rule)":
Question (front)
Which formula describes RGT Rule (Q10 Rule)?
Answer in your set
Question (front)
How do you rearrange v₂ = v₁·Q₁₀^(ΔT/10 K) for Temperature coefficient?
Answer in your set
Question (front)
Which common mistake happens with RGT Rule (Q10 Rule)?
Answer in your set
+ 7 more cards: units, variables, derivation, example, exam task
These 10 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.
Scientific sources
Common notations & search queries
Related formulas
More Chemistry formulas
Frequently asked questions about RGT Rule (Q10 Rule)
How do you calculate with the Q10 rule?+
First determine the temperature change ΔT and divide it by 10 K: that gives the number of doubling steps. Then raise the temperature coefficient Q₁₀ to this power: v₂ = v₁·Q₁₀^(ΔT/10 K). Example with Q₁₀ = 2: heating from 20 °C to 50 °C means ΔT = 30 K, three steps and a factor of 2³ = 8. A reaction that previously took 24 minutes is then finished in about 3 minutes. Non-integer steps work too: for ΔT = 15 K you calculate 2^1.5 ≈ 2.8. On cooling, ΔT becomes negative and the factor smaller than 1, the reaction slows down.
Why does the reaction rate roughly double every 10 kelvin?+
The reason lies in the Arrhenius equation: only particles with enough energy overcome the activation barrier, and their fraction depends exponentially on temperature. Even slight heating shifts the particles' energy distribution so that markedly more collisions succeed. For typical activation energies around 50 kJ/mol, the Arrhenius calculation near room temperature yields just about a factor of 2 per 10 K; that is exactly where the rule of thumb comes from. It is therefore not a separate law of nature but a special case. Reactions with higher activation energy respond more sensitively to temperature (Q₁₀ closer to 3 or 4), those with a low barrier more weakly (Q₁₀ nearer 1.5 to 2).
How accurate is the Q10 rule and when does it fail?+
The Q10 rule is a deliberately coarse rule of thumb for estimates, not an exact formula. It works best between about 0 and 100 °C and over moderate spans of a few tens of kelvin. Strictly speaking its Q₁₀ is itself temperature dependent, because the exact Arrhenius equation does not deliver a constant factor per 10 K; over wide ranges the two calculations diverge. The rule fails completely for enzymes and microorganisms above the temperature optimum: there the protein denatures and the rate collapses instead of rising further. Diffusion-controlled and explosive reactions do not follow it either. For exams: estimate with the Q10 rule, calculate exactly with Arrhenius.
What exactly is the temperature coefficient Q10?+
Q₁₀ is the factor by which the reaction rate increases when the temperature rises by exactly 10 K. For many chemical reactions it lies between 2 and 4, for purely physical processes such as diffusion only around 1.2 to 1.5. You determine it experimentally from two rate measurements: Q₁₀ = (v₂/v₁)^(10 K/ΔT). In biology, Q₁₀ is an important parameter for metabolic rates: the heartbeat and respiration of cold-blooded animals follow it, as does the germination speed of seeds. A high Q₁₀ indicates a high activation energy of the underlying process, because the two quantities are linked through the Arrhenius equation.
Why does food keep longer in the fridge?+
Spoilage is chemistry: enzymatic reactions, oxidations and the metabolism of microorganisms run faster the warmer it is. Cooling reverses the RGT rule. From 20 °C room temperature to 5 °C fridge temperature is ΔT = −15 K; with Q₁₀ = 3, as is typical for many biological processes, the factor is 3^(−1.5) ≈ 0.19. The spoilage reactions therefore run at only about one fifth of the rate, and the shelf life rises correspondingly to roughly five times. In the freezer the effect is even stronger; additionally, freezing the water drastically restricts the mobility of the molecules. Exactly the same logic is used in reverse when cooking and in accelerated ageing tests, where heating deliberately speeds up the processes.
Retain RGT Rule (Q10 Rule) for exams
Create a curated FSRS exam set for v₂ = v₁·Q₁₀^(ΔT/10 K): formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
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How do you calculate with RGT Rule (Q10 Rule)?
Here is how to work through a typical RGT Rule (Q10 Rule) (v₂ = v₁·Q₁₀^(ΔT/10 K)) task step by step:
- 1
Task
Q₁₀ = 2: by what factor does heating from 25 °C to 55 °C speed things up?
Solution path
ΔT = 30 K, that is three 10 K steps: v₂/v₁ = 2³ = 8. The reaction runs about eight times faster.
- 2
Task
Q₁₀ = 3: how much does cooling from 20 °C to 5 °C slow things down?
Solution path
ΔT = −15 K → factor 3^(−1.5) = 1/(3·√3) ≈ 0.19. The reaction runs at only about one fifth of the rate; that is why chilled food keeps longer.
v₂ = v₁·Q₁₀^(ΔT/10 K) · 10 cards ready
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