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).
Amount of Substance (Mole)
The amount of substance n counts particles in portions of moles. Calculated from mass m and molar mass M, n = m/M is the basic formula of every stoichiometric calculation.
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Formula
n = \frac{m}{M}Variables & units – Amount of Substance (Mole)
| Symbol | Meaning | Unit |
|---|---|---|
| n | Amount of substance | mol |
| m | Mass of the substance portion | g |
| M | Molar mass (from the periodic table) | g/mol |
Derivation & background – Amount of Substance (Mole)
Since the 2019 SI reform the mole has been defined via the Avogadro constant: 1 mol contains exactly 6.02214076×10²³ particles. The amount of substance translates weighable masses into countable particle portions and thus makes reaction equations calculable, because reactions proceed in particle ratios, not mass ratios.
Exam blueprint
Validity range
Applies to any pure portion of substance; M must match the particle type counted (atom, molecule or formula unit). For mixtures it applies only per component.
Derivation steps
The molar mass is defined as mass per amount of substance; solving for n gives the formula.
- 1Definition of molar mass: M = m/n links mass and particle portion.
- 2Rearranging for the amount of substance gives n = m/M.
Rearrangements
Mass
This is how you calculate the mass to weigh in from a desired amount of substance.
Molar mass
This is how you identify unknown substances from measured values.
Task variant
How many moles are 8.0 g of NaOH (M = 40.0 g/mol)?
n = m/M = 8.0 g / 40.0 g/mol = 0.20 mol.
What is the mass of 0.25 mol of calcium carbonate CaCO₃ (M = 100.1 g/mol)?
m = n·M = 0.25 mol · 100.1 g/mol ≈ 25.0 g.
Common mistakes
Inserting the mass in kg while M is in g/mol.
Match units: insert m in grams or convert M.
Using M for the wrong particle type, for example O instead of O₂.
Always determine M for the actual formula: M(O₂) = 32.00 g/mol.
Equating amount of substance and mass.
n counts particle portions in moles; the mass additionally depends on M.
Exam context
- Almost every stoichiometry task starts with n = m/M: yields, limiting reactants, gas volumes, titration.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Stoichiometry backbone
n = m/M, c = n/V and N = n·N_A form the triangle of chemical amount calculations.
Worked example
8.0 g of sodium hydroxide NaOH (M = 40.0 g/mol): n = m/M = 8.0/40.0 = 0.20 mol. Conversely: 0.5 mol of water (M = 18.0 g/mol) weighs m = n·M = 0.5·18.0 = 9.0 g.
Applications
Stoichiometry (reaction equations), preparing solutions, titration, gas calculations, pharmaceutical dosing
Quanta exam set
Curated exam set for "Amount of Substance (Mole)":
Question (front)
Which formula describes Amount of Substance (Mole)?
Answer in your set
Question (front)
How do you rearrange n = m/M for Mass?
Answer in your set
Question (front)
Which common mistake happens with Amount of Substance (Mole)?
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 Amount of Substance (Mole)
How do you calculate the amount of substance from the mass?+
Divide the mass by the molar mass: n = m/M. Insert the mass m in grams and the molar mass M in g/mol from the periodic table, then the amount of substance comes out in moles. Example: 8.0 g of sodium hydroxide NaOH with M = 40.0 g/mol give n = 8.0/40.0 = 0.20 mol. For compounds you first calculate M as the sum of the atomic masses, for NaOH 22.99 + 16.00 + 1.008 ≈ 40.0 g/mol. Make sure mass and molar mass refer to the same particle type: if you want the amount of oxygen gas, use M(O₂) = 32.00 g/mol, not M(O) = 16.00 g/mol.
What exactly is a mole?+
A mole is a counting unit for particles, just as a dozen denotes twelve items. Since the 2019 SI reform, one mole contains exactly 6.02214076×10²³ particles, the Avogadro constant. The trick of this number: one mole of carbon-12 weighs almost exactly 12 g, one mole of water about 18 g. So the molar mass in g/mol has the same numerical value as the atomic or molecular mass in u. This lets you count particles with a balance: weigh out 58.44 g of table salt and you have exactly one mole of NaCl formula units. Chemical reactions proceed in particle ratios, which is why all of stoichiometry calculates in moles.
Why does chemistry calculate with amounts of substance instead of masses?+
Because reaction equations describe particle ratios, not mass ratios. In 2 H₂ + O₂ → 2 H₂O two hydrogen molecules react with one oxygen molecule, but by no means two grams with one gram: by mass it is 4 g of hydrogen per 32 g of oxygen. The amount of substance translates the unwieldy particle number into a weighable quantity and makes the coefficients of the equation directly usable: n(H₂)/n(O₂) = 2/1. The typical exam procedure is therefore: convert the given mass into an amount with n = m/M, apply the ratio of coefficients, and finally translate back into the required mass with m = n·M.
How are amount of substance, mass and particle number related?+
The amount of substance n is the central hub: m = n·M with the molar mass M leads to the mass, N = n·N_A with the Avogadro constant N_A = 6.022×10²³ mol⁻¹ leads to the particle number. Example water: 9.0 g correspond to n = 9.0/18.0 = 0.50 mol, which is N = 0.5·6.022×10²³ ≈ 3.0×10²³ molecules. To go from particle number to mass, always route via the amount of substance: first n = N/N_A, then m = n·M. For gases there is a third bridge: at standard conditions one mole of gas occupies about 22.4 L, so amounts can also be obtained from volumes.
Which mistakes happen most often with n = m/M?+
Three classics. First, mixing units: inserting the mass in kilograms while M is in g/mol puts you off by a factor of 1000; always convert m to grams. Second, the wrong particle type: for chlorine gas M(Cl₂) = 70.9 g/mol applies, not 35.45 g/mol; for salts the complete formula unit counts, including all indices, so 74.10 g/mol for Ca(OH)₂. Third, conceptually confusing amount and mass: 1 mol of lead and 1 mol of helium contain the same number of particles but weigh 207 g and 4 g respectively. A quick plausibility check helps: verify the result unit (mol) and estimate the order of magnitude in your head.
Retain Amount of Substance (Mole) for exams
Create a curated FSRS exam set for n = m/M: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
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How do you calculate with Amount of Substance (Mole)?
Here is how to work through a typical Amount of Substance (Mole) (n = m/M) task step by step:
- 1
Task
How many moles are 8.0 g of NaOH (M = 40.0 g/mol)?
Solution path
n = m/M = 8.0 g / 40.0 g/mol = 0.20 mol.
- 2
Task
What is the mass of 0.25 mol of calcium carbonate CaCO₃ (M = 100.1 g/mol)?
Solution path
m = n·M = 0.25 mol · 100.1 g/mol ≈ 25.0 g.
n = m/M · 10 cards ready
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