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 citation-first source protocol: every AI-generated card declares its source (source title, type, confidence score of at least 0.9) BEFORE the card is generated. No content ships without verified source coverage. This is a standard we have not seen in other AI study tools. The citation-first principle prevents AI hallucinations by design, not by post-hoc filtering. Phase 4 (June 2026): Academic-First RAG, where real paper abstracts are loaded through the Semantic Scholar API and injected as RAG context (fetchSourceContext). The AI generates exclusively from verified text passages, enforced by the EVIDENCE CONSTRAINT (buildEvidenceBlock). Temperature is set to 0 and thinkingBudget to 0 in RAG mode. Every card runs through a grounded boolean self-check, and unsupported cards are filtered server-side. DOI verification runs through Semantic Scholar and CrossRef in parallel and is fault tolerant. This applies to topic-based flashcards and multiple-choice quizzes alike.
(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 citation-first AI generation, 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) | Citation-first: source declared BEFORE generation, 5-tier authority hierarchy, confidence threshold 0.9. Phase 4: Academic-First RAG (Semantic Scholar abstracts as context, temperature 0, grounded self-check, server-side filtering) | 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 citation-first source 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, citation-first, 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 May 2026).
Kc = [C]ᶜ·[D]ᵈ / ([A]ᵃ·[B]ᵇ) – Law of Mass Action (Equilibrium Constant): formula, derivation, units and application
The law of mass action describes chemical equilibrium: the stoichiometrically weighted ratio of the equilibrium concentrations of products to reactants is constant. The formula Kc = [C]ᶜ·[D]ᵈ / ([A]ᵃ·[B]ᵇ) belongs to Chemistry (Reaction Kinetics) and sits at the bachelor level. Guldberg and Waage (1864) formulated the law of mass action. Kc >> 1: equilibrium on the product side. Kc << 1: equilibrium on the reactant side. Le Chatelier's principle: a change in concentration,…
Variables and units: Kc stands for Equilibrium constant (concentration-based) in the unit dimensionslos. [C],[D] stands for Equilibrium concentrations of the products in the unit mol/L. [A],[B] stands for Equilibrium concentrations of the reactants in the unit mol/L. a,b,c,d stands for Stoichiometric coefficients in the unit dimensionslos. Kc dimensionless (concentrations in mol/L)
Worked example: N₂ + 3H₂ ⇌ 2NH₃. With [N₂] = 0.5, [H₂] = 0.5, [NH₃] = 0.1 mol/L: Kc = (0.1)² / (0.5·(0.5)³) = 0.01/0.0625 = 0.16.
Applications: The Haber-Bosch process (NH₃ synthesis), acid-base equilibria, buffer calculation, the solubility product. The formula Law of Mass Action (Equilibrium Constant) is needed at university and in exams and is part of the Quanta STEM formula sheet with a full derivation, a variable table and a flashcard function.
Law of Mass Action (Equilibrium Constant)
The law of mass action describes chemical equilibrium: the stoichiometrically weighted ratio of the equilibrium concentrations of products to reactants is constant.
Formula
K_c = \frac{[C]^c \cdot [D]^d}{[A]^a \cdot [B]^b}Variables & units – Law of Mass Action (Equilibrium Constant)
| Symbol | Meaning | Unit |
|---|---|---|
| Kc | Equilibrium constant (concentration-based) | dimensionslos |
| [C],[D] | Equilibrium concentrations of the products | mol/L |
| [A],[B] | Equilibrium concentrations of the reactants | mol/L |
| a,b,c,d | Stoichiometric coefficients | dimensionslos |
Derivation & background – Law of Mass Action (Equilibrium Constant)
Guldberg and Waage (1864) formulated the law of mass action. Kc >> 1: equilibrium on the product side. Kc << 1: equilibrium on the reactant side. Le Chatelier's principle: a change in concentration, pressure or temperature shifts the equilibrium so as to compensate.
Worked example
N₂ + 3H₂ ⇌ 2NH₃. With [N₂] = 0.5, [H₂] = 0.5, [NH₃] = 0.1 mol/L: Kc = (0.1)² / (0.5·(0.5)³) = 0.01/0.0625 = 0.16.
Applications
The Haber-Bosch process (NH₃ synthesis), acid-base equilibria, buffer calculation, the solubility product
Quanta flashcard tip
Ideal flashcard for "Law of Mass Action (Equilibrium Constant)":
Question (front)
What does the formula Kc = [C]ᶜ·[D]ᵈ / ([A]ᵃ·[B]ᵇ) describe? Name every variable and unit.
Answer (back)
The law of mass action describes chemical equilibrium: the stoichiometrically weighted ratio of the equilibrium concentrations of products to reactants is constant.. Kc: Equilibrium constant (concentration-based) (dimensionslos); [C],[D]: Equilibrium concentrations of the products (mol/L); [A],[B]: Equilibrium concentrations of the reactants (mol/L); a,b,c,d: Stoichiometric coefficients (dimensionslos).
Scientific sources
Common notations & search queries
Related formulas
More Chemistry formulas
Frequently asked questions about Law of Mass Action (Equilibrium Constant)
What does the formula Law of Mass Action (Equilibrium Constant) (Kc = [C]ᶜ·[D]ᵈ / ([A]ᵃ·[B]ᵇ)) describe?+
The law of mass action describes chemical equilibrium: the stoichiometrically weighted ratio of the equilibrium concentrations of products to reactants is constant.
Which variables does Law of Mass Action (Equilibrium Constant) have?+
Kc: Equilibrium constant (concentration-based) (dimensionslos) · [C],[D]: Equilibrium concentrations of the products (mol/L) · [A],[B]: Equilibrium concentrations of the reactants (mol/L) · a,b,c,d: Stoichiometric coefficients (dimensionslos)
Where is Law of Mass Action (Equilibrium Constant) applied?+
The Haber-Bosch process (NH₃ synthesis), acid-base equilibria, buffer calculation, the solubility product
What is Law of Mass Action (Equilibrium Constant) in LaTeX?+
\[ K_c = \frac{[C]^c \cdot [D]^d}{[A]^a \cdot [B]^b} \], copy-ready for LaTeX documents and Quanta flashcards.
Retain Law of Mass Action (Equilibrium Constant) for good
Create an FSRS-optimized flashcard for Kc = [C]ᶜ·[D]ᵈ / ([A]ᵃ·[B]ᵇ) in Quanta. The algorithm shows you the formula exactly when you are about to forget it, for 80 to 95% long-term retention.
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