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).
Molar Concentration
The molar concentration c (molarity) states how many moles of a dissolved substance are contained in one litre of solution. It connects amount of substance and solution volume.
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Formula
c = \frac{n}{V}Variables & units – Molar Concentration
| Symbol | Meaning | Unit |
|---|---|---|
| c | Molar concentration (molarity) | mol/L |
| n | Amount of the dissolved substance | mol |
| V | Volume of the finished solution | L |
Derivation & background – Molar Concentration
V is the volume of the finished solution, not of the added solvent; that is why a volumetric flask is filled up to the mark. With n = m/M it follows that c = m/(M·V). On dilution the amount of substance is conserved, giving c₁·V₁ = c₂·V₂. Colloquially a solution with c = 1 mol/L is called "one-molar".
Exam blueprint
Validity range
Applies to homogeneous solutions; V is the volume of the finished solution at a given temperature, not that of the solvent.
Derivation steps
The concentration normalizes the dissolved amount of substance to the solution volume so that samples become comparable.
- 1The withdrawn amount of substance n grows proportionally with the withdrawn volume.
- 2The quotient c = n/V is therefore independent of volume and characterizes the solution.
Rearrangements
Amount of substance
The core of every titration calculation.
Concentration from weighed mass
Combination with n = m/M for lab practice.
Dilution
The amount of substance is conserved on dilution.
Task variant
5.85 g of NaCl in 500 mL of solution: what is c?
n = 5.85/58.44 = 0.100 mol; c = 0.100 mol / 0.500 L = 0.200 mol/L.
How many moles of HCl are in 25 mL of hydrochloric acid with c = 0.8 mol/L?
n = c·V = 0.8 mol/L · 0.025 L = 0.020 mol.
Common mistakes
Inserting the volume in millilitres.
Convert V to litres: 250 mL = 0.250 L.
Using the volume of the solvent instead of the solution.
Fill up to the final volume in the volumetric flask; c refers to the finished solution.
Confusing mass concentration β = m/V with c.
c counts moles, β grams; convert via c = β/M.
Exam context
- Titration and volumetric analysis, dilution series and pH tasks with strong acids and bases.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Amount calculations in solution
Connects amount of substance, weighed mass and pH calculation in everyday lab work.
Worked example
5.85 g of NaCl (M = 58.44 g/mol) in 500 mL of solution: n = 5.85/58.44 = 0.100 mol → c = n/V = 0.100/0.500 = 0.200 mol/L.
Applications
Volumetric analysis and titration, preparing laboratory solutions, infusion and drug dosing, pH calculations, buffer preparation
Quanta exam set
Curated exam set for "Molar Concentration":
Question (front)
Which formula describes Molar Concentration?
Answer in your set
Question (front)
How do you rearrange c = n/V for Amount of substance?
Answer in your set
Question (front)
Which common mistake happens with Molar Concentration?
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 Molar Concentration
How do you calculate the molar concentration of a solution?+
Divide the dissolved amount of substance by the volume of the solution: c = n/V, with n in moles and V in litres. If the weighed mass is given instead of the amount, first convert with n = m/M. Example: 5.85 g of table salt (M = 58.44 g/mol) are dissolved in water and filled up to 500 mL. Then n = 5.85/58.44 = 0.100 mol and c = 0.100/0.500 = 0.200 mol/L. Two pitfalls: the volume must be in litres, so 500 mL = 0.500 L, and what counts is the volume of the finished solution, not the amount of water added. That is why you fill up to the calibration mark in a volumetric flask.
What does a 1-molar solution mean?+
A 1-molar solution contains exactly one mole of the dissolved substance per litre of solution, i.e. c = 1 mol/L. The phrase "molar" (abbreviated M) is common in the lab: a 0.1-molar hydrochloric acid has c(HCl) = 0.1 mol/L. The reference to the solution volume matters: for a 1-molar NaCl solution you weigh out 58.44 g of NaCl, dissolve it in some water and then fill up to exactly one litre. You do not add one litre of water, because the dissolved substance changes the volume. Also beware of the term molality: it refers to the mass of the solvent in mol/kg and is a different quantity.
How do you calculate a dilution with c₁·V₁ = c₂·V₂?+
When diluting, the dissolved amount of substance stays the same, only the volume grows. From n = c·V it follows that c₁·V₁ = c₂·V₂. Example: you need 250 mL of hydrochloric acid with c = 0.1 mol/L and have a stock solution with c = 1.0 mol/L. Then V₁ = c₂·V₂/c₁ = 0.1·0.250/1.0 = 0.025 L. So you pipette 25 mL of stock solution into a 250 mL volumetric flask and fill up to the mark with water. The formula applies to pure dilution without reaction. Practical rule for acids: always add the acid to the water, never the other way round, because of the heat of dilution.
What is the difference between molar concentration and mass concentration?+
The molar concentration c = n/V counts moles per litre, the mass concentration β = m/V counts grams per litre. Both refer to the solution volume but differ by the molar mass: β = c·M. A saline solution with c = 0.15 mol/L thus has β = 0.15·58.44 ≈ 8.8 g/L; that roughly corresponds to physiological saline (9 g/L). For chemical calculations c is the more useful quantity, because reactions proceed in ratios of amounts; on packaging and in medicine, however, you often encounter β. In tasks, therefore, check carefully which concentration is meant and convert via M if necessary.
How do you use c = n/V in a titration?+
In a titration you determine an unknown concentration from the consumption of a standard solution of known concentration. At the equivalence point, for a 1:1 reaction such as HCl + NaOH: n(acid) = n(base), i.e. c₁·V₁ = c₂·V₂. Example: 20.0 mL of hydrochloric acid of unknown concentration consume 12.5 mL of sodium hydroxide with c = 0.1 mol/L. Then n(NaOH) = 0.1·0.0125 = 1.25×10⁻³ mol = n(HCl), and c(HCl) = 1.25×10⁻³/0.0200 = 0.0625 mol/L. For other stoichiometries, such as sulfuric acid with two protons, you must build in the ratio from the equation: n(NaOH) = 2·n(H₂SO₄). This is exactly where most exam errors happen.
Retain Molar Concentration for exams
Create a curated FSRS exam set for c = n/V: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
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How do you calculate with Molar Concentration?
Here is how to work through a typical Molar Concentration (c = n/V) task step by step:
- 1
Task
5.85 g of NaCl in 500 mL of solution: what is c?
Solution path
n = 5.85/58.44 = 0.100 mol; c = 0.100 mol / 0.500 L = 0.200 mol/L.
- 2
Task
How many moles of HCl are in 25 mL of hydrochloric acid with c = 0.8 mol/L?
Solution path
n = c·V = 0.8 mol/L · 0.025 L = 0.020 mol.
c = n/V · 10 cards ready
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