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)

FeatureQuantaAnkiQuizletRemNoteKnowtChatGPT
AlgorithmFSRS-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 availableNo published algorithmNo 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 cardNot availableNot availableNot availableNot availablePost-hoc citations without verification
Bloom taxonomy constraintLevels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural levelNo controlNo controlNo controlNo controlNo control
Distractor validation (MC)Every incorrect answer checked for plausibility (Haladyna and Downing 1989)Not availableNot availableNot availableNot availableNot available
AI tutor methodologySocratic method: counter-questions only, no direct answers (Chi et al. 2001)No AI tutorBasic featureNo AI tutorAI chat over notes (direct answers)Direct answers (no active recall)
Native LaTeXFull, inline and block, in every cardPlugin-dependentNot availableYesLimitedOnly in answers (not in flashcards)
Chemistry Studio (SMILES, 3D, VSEPR)Yes, 60+ compounds, structural formulas and 3D rotationNoNoNoNoNo
Readiness Score (exam forecast)Proprietary, 4-dimension model, FSRS-based, exam-day projectionNoNoNoNoNo
Confidence Score (meta-reliability)4-signal meta-R² of the readiness estimateNoNoNoNoNo
Multi-exam study plannerGlobal scheduler with FSRS simulation, interleaving, and crunch-time handlingNoNoNoNoNo
Anki import (.apkg)Yes, completeNativeNoNoNoNo
AI cards from your notes and PDFsYes, with the source-first verbatim quote-match protocolNoLimitedYes, no source protocolYes, no source protocolYes, no scheduling
Price (monthly, annual)Basic: free forever, Pro: 6 euros per monthFree on desktop, 25 dollars on iOSabout 3 euros per month (annual)about 8 dollars per monthfree tier, about 10 dollars per month20 dollars per month (Plus)
Standalone calculation engineYes, 900 LOC of TypeScript, 4 modules, no API dependencyYes (SM-2)NoPartial (FSRS fork)UnknownNo (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).

Chemistry · Chemical Equilibrium

Solubility Product

The solubility product K_L is the equilibrium constant for dissolving a sparingly soluble salt: the product of the ion concentrations, each raised to its stoichiometric coefficient.

AdvancedExam-relevant

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Formula

K_L = [A]ᵃ·[B]ᵇ
LaTeX: K_L = [A^{m+}]^a \cdot [B^{n-}]^b
K_L in (mol/L)^(a+b) · [A^m+] in mol/L · [B^n-] in mol/L · a, b dimensionless

Variables & units – Solubility Product

SymbolMeaningUnit
K_LSolubility product (temperature-dependent)(mol/L)^(a+b)
[A^m+]Equilibrium concentration of the cationmol/L
[B^n-]Equilibrium concentration of the anionmol/L
a, bStoichiometric coefficients of the ionsdimensionslos

Derivation & background – Solubility Product

The solubility product follows from the law of mass action for the equilibrium AaBb(s) ⇌ a A^m+ + b B^n-. The undissolved solid has activity 1 and therefore does not appear. Comparing the ion product Q with K_L decides: Q < K_L unsaturated, Q = K_L saturated, Q > K_L precipitation. Tables often list pK_L = −lg K_L; K_L is temperature-dependent.

Exam blueprint

Validity range

Applies to saturated solutions of sparingly soluble salts in equilibrium with the solid; rigorously with activities, in school with concentrations, and only at fixed temperature.

Derivation steps

The law of mass action is applied to the dissolution equilibrium; the pure solid has activity 1.

  1. 1For AaBb(s) ⇌ a A^m+ + b B^n- the solid does not appear in the mass-action fraction.
  2. 2What remains is the product of the ion concentrations: K_L = [A^m+]^a·[B^n-]^b.

Rearrangements

Solubility of a 1:1 salt

s = \sqrt{K_L}

For AgCl or BaSO₄: both ion concentrations equal s.

Solubility of an AB₂ salt

s = \sqrt[3]{\frac{K_L}{4}}

K_L = s·(2s)² = 4s³, for example for Mg(OH)₂ or PbCl₂.

pK_L

pK_L = -\lg K_L

Large pK_L values mean sparingly soluble salts.

Task variant

What is the solubility of AgCl (K_L = 1.7×10⁻¹⁰ mol²/L²)?

s = √K_L = √(1.7×10⁻¹⁰) ≈ 1.3×10⁻⁵ mol/L. With M = 143.3 g/mol this is 1.3×10⁻⁵·143.3 ≈ 1.9×10⁻³ g/L, about 1.9 mg of AgCl per litre.

Calculate the solubility of Mg(OH)₂ (K_L = 5.6×10⁻¹² mol³/L³).

K_L = [Mg²⁺]·[OH⁻]² = s·(2s)² = 4s³ → s = ∛(K_L/4) = ∛(1.4×10⁻¹²) ≈ 1.1×10⁻⁴ mol/L. The factor 2 in front of s must be squared as well.

Common mistakes

Treating the stoichiometric coefficients as factors instead of exponents.

In the K_L expression the coefficients become exponents: for PbCl₂ it is [Pb²⁺]·[Cl⁻]².

Directly comparing K_L values of salts with different formula types.

Only salts of the same type are directly comparable; otherwise compute the solubility s first, the units of K_L differ.

Writing the solid into the equilibrium expression.

The solid has activity 1 and never appears in the K_L expression.

Ignoring the common-ion effect.

Other sources of the same ion lower the solubility, because the ion product must not exceed K_L.

Exam context

  • Precipitation yes/no via comparing the ion product Q with K_L, solubility in pure water and with a common ion, ion detection.

These mistakes cost points in real exams. The set drills them until they stick.

Formula cluster

Heterogeneous equilibria

Applies the law of mass action to precipitation and dissolution.

Worked example

AgCl at 25 °C: K_L = 1.7×10⁻¹⁰ mol²/L². Solubility s = √K_L = 1.3×10⁻⁵ mol/L; with M = 143.3 g/mol only about 1.3×10⁻⁵·143.3 ≈ 1.9 mg of AgCl dissolve per litre of water.

Applications

Precipitation reactions and ion detection, water softening, gravimetric analysis, limescale and stalactite formation, kidney stones in medicine

Quanta exam set

Curated exam set for "Solubility Product":

Question (front)

Which formula describes Solubility Product?

Answer in your set

Question (front)

How do you rearrange K_L = [A]ᵃ·[B]ᵇ for Solubility of a 1:1 salt?

Answer in your set

Question (front)

Which common mistake happens with Solubility Product?

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

KL = [A+]^a * [B-]^bKspLöslichkeitsprodukt berechnenLöslichkeit aus KLsolubility productpKL WertFällung Niederschlag Formelschwer lösliche SalzeIonenprodukt Löslichkeit

Related formulas

More Chemistry formulas

Frequently asked questions about Solubility Product

How do you calculate the solubility from the solubility product?+

Insert the unknown solubility s for each ion concentration, weighted by its stoichiometric coefficient, and solve for s. For a 1:1 salt like AgCl, K_L = s·s = s², so s = √K_L: with K_L = 1.7×10⁻¹⁰ mol²/L² you get s = 1.3×10⁻⁵ mol/L. For an AB₂ salt like Mg(OH)₂ each formula unit releases two hydroxide ions: K_L = s·(2s)² = 4s³, so s = ∛(K_L/4). The factor in front of s must be raised to the power as well, which is the most common calculation error. Finally you can convert s with the molar mass into a mass solubility in grams per litre.

When does a precipitate form?+

Compare the ion product Q with the solubility product K_L. Q is formed exactly like K_L, but with the concentrations actually present instead of the equilibrium values. If Q is smaller than K_L, the solution is unsaturated and more salt can dissolve. If Q equals K_L, the solution is saturated and in equilibrium with the solid. If Q exceeds the solubility product, the solution is supersaturated and the salt precipitates until Q has dropped back to K_L. When combining two solutions you must account for dilution first, because the volumes add up and lower the individual concentrations.

Why does the solid not appear in the solubility product?+

Because the law of mass action rigorously works with activities, and the activity of a pure solid is by definition 1. The solid has a fixed density and thus a constant "concentration" that does not change during dissolution; it is therefore absorbed into the constant. What remains is only the product of the ion concentrations in solution. This has a practical consequence: it does not matter whether one gram or one kilogram of undissolved salt sits at the bottom, the saturation concentrations above it are identical. All that matters is that some solid is present at all, so the equilibrium between dissolving and precipitating can exist.

What does a common ion do?+

A common ion lowers the solubility. If you add table salt to a saturated AgCl solution, [Cl⁻] rises sharply. Since the product [Ag⁺]·[Cl⁻] must not exceed K_L, [Ag⁺] has to fall accordingly: AgCl precipitates. Numerically, at [Cl⁻] = 0.1 mol/L only [Ag⁺] = K_L/[Cl⁻] = 1.7×10⁻¹⁰/0.1 = 1.7×10⁻⁹ mol/L remains, roughly ten thousand times less than in pure water. This is Le Chatelier's principle applied to the dissolution equilibrium, and it is used deliberately in analytical chemistry to make precipitations as complete as possible, for example in gravimetric determinations.

Can you compare K_L values of different salts directly?+

Only if the salts have the same formula type. For two 1:1 salts like AgCl and AgBr, the smaller K_L really does mean the lower solubility. As soon as the stoichiometry differs, however, the K_L values carry different units and powers: an AB₂ salt with K_L in mol³/L³ cannot be compared directly with a 1:1 salt in mol²/L². In that case you must calculate the molar solubility s for both and compare those values. An example: with 5.6×10⁻¹², Mg(OH)₂ has a numerically smaller K_L than some 1:1 salts, but because of the relation s = ∛(K_L/4) it is not necessarily less soluble.

Retain Solubility Product for exams

Create a curated FSRS exam set for K_L = [A]ᵃ·[B]ᵇ: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.

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How do you calculate with Solubility Product?

Here is how to work through a typical Solubility Product (K_L = [A]ᵃ·[B]ᵇ) task step by step:

  1. 1

    Task

    What is the solubility of AgCl (K_L = 1.7×10⁻¹⁰ mol²/L²)?

    Solution path

    s = √K_L = √(1.7×10⁻¹⁰) ≈ 1.3×10⁻⁵ mol/L. With M = 143.3 g/mol this is 1.3×10⁻⁵·143.3 ≈ 1.9×10⁻³ g/L, about 1.9 mg of AgCl per litre.

  2. 2

    Task

    Calculate the solubility of Mg(OH)₂ (K_L = 5.6×10⁻¹² mol³/L³).

    Solution path

    K_L = [Mg²⁺]·[OH⁻]² = s·(2s)² = 4s³ → s = ∛(K_L/4) = ∛(1.4×10⁻¹²) ≈ 1.1×10⁻⁴ mol/L. The factor 2 in front of s must be squared as well.

K_L = [A]ᵃ·[B]ᵇ · 10 cards ready

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